CN103236191B - Video-based safety precaution method for vehicle merging from highway ramp - Google Patents

Abstract

The invention belongs to the field of video detecting, and provides a video-based safety precaution method for vehicle merging from a highway ramp. The method includes arranging two cameras in the same directions as two ways on highway entrances to detect moving vehicles, marking vehicle trajectories, acquiring actual moving distance through calculation according to the vehicle trajectories in a continuous tracking frame, acquiring actual speed of the vehicles, acquiring time difference of vehicle entering the merging areas between the two ways through the speeds of vehicles on the two ways, and judging possibility of vehicle collisions through the time difference. Compared with the prior art, the video-based safety precaution method is not limited by environments, capable of performing real-time and reliable precaution for all vehicles in the video areas, easy to realize, high in accuracy, applicable to real-time traffic precaution for highway ramps, and wide in application prospect.

Description

Translated fromChinese

一种基于视频的高速公路匝道车辆并入安全预警方法A video-based safety warning method for vehicles merging on freeway ramps

技术领域technical field

本发明属于视频检测领域，具体涉及一种基于视频的高速公路匝道车辆并入安全预警方法。The invention belongs to the field of video detection, and in particular relates to a video-based safety warning method for vehicle merging on a freeway ramp.

背景技术Background technique

近来，对着我国高速公路里程的飞速增长，高速公路交通事故逐年增加，其中高速公路匝道口是事故多发点段。特别是在光线、天气较差的情况下，匝道口极易发生交通事故。基于视频的高速公路匝道车辆并入安全预警技术提高了异常状况的迅速反应能力，同时可以将实时获取的数据及时准确的传输到交通管理部门，提高工作效率和整个路网的运营水平。Recently, facing the rapid growth of expressway mileage in our country, expressway traffic accidents are increasing year by year, among which the ramp of expressway is the accident-prone section. Especially in poor light and weather conditions, ramp crossings are prone to traffic accidents. The incorporation of video-based expressway ramp vehicles into safety warning technology improves the rapid response capability of abnormal conditions, and at the same time, real-time acquired data can be transmitted to the traffic management department in a timely and accurate manner, improving work efficiency and the operation level of the entire road network.

现有的方法主要是由驾驶员直接对匝道口的交通信息进行判断，但结果易受到驾驶人员的主观影响，不但反应慢，且对光线、天气等影响具有较强的适应性，无法满足实际应用的需要。The existing method mainly uses the driver to directly judge the traffic information at the ramp intersection, but the result is easily affected by the driver's subjective influence, not only slow in response, but also has strong adaptability to the influence of light and weather, which cannot meet the actual situation. application needs.

发明内容Contents of the invention

针对现有技术存在的缺陷和不足，本发明的目的在于，提供一种基于视频的高速公路匝道车辆并入安全预警方法，该方法可以对视频范围内所有的车辆实现实时、可靠的预警。Aiming at the defects and deficiencies in the prior art, the object of the present invention is to provide a video-based method for safety early warning of highway ramp vehicle merging, which can realize real-time and reliable early warning for all vehicles within the video range.

为了实现上述任务，本发明采用如下技术方案予以实现：In order to realize above-mentioned task, the present invention adopts following technical scheme to realize:

一种基于视频的高速公路匝道车辆并入安全预警方法，该方法按照以下步骤进行：A video-based expressway ramp vehicle merging safety warning method, the method is carried out according to the following steps:

步骤一，在高速路的匝道口沿着主干道方向架设第一摄像机，第一摄像机的射程记为L₁，第一摄像机采集主干道上的交通视频图像，记为第一视频图像，采用基于特征的目标车辆跟踪方法对摄像机射程范围内的所有驶向并入区域的所有车辆进行跟踪得到所有辆车的运动轨迹；Step 1: Set up the first camera along the direction of the main road at the ramp entrance of the expressway. The range of the first camera is recorded as L₁ . The first camera collects the traffic video image on the main road, which is recorded as the first video image. The characteristic target vehicle tracking method tracks all the vehicles within the range of the camera to the merged area to obtain the movement trajectories of all vehicles;

所述的基于特征的目标车辆跟踪方法即：采用基于像素块的帧间差分法检测运动车辆，对检测出的运动图像利用Moravec算法提取相应的角点作为目标位置，并对相应的角点进行匹配跟踪；The described target vehicle tracking method based on features is: adopt the inter-frame difference method based on the pixel block to detect the moving vehicle, utilize the Moravec algorithm to extract the corresponding corner points as the target position for the detected moving images, and perform corresponding corner points match tracking;

步骤二，在高速路的匝道口第一摄像机架设处沿着匝道方向架设第二摄像机，第二摄像机的射程记为L₂，第二摄像机采集匝道上的交通视频图，记为第二视频图像，采用基于特征的目标车辆跟踪方法对摄像机射程范围内的所有驶向并入区域的所有车辆进行跟踪得到所有辆车的运动轨迹；Step 2: Set up a second camera along the direction of the ramp at the place where the first camera is installed at the ramp entrance of the expressway. The range of the second camera is denoted as L₂ . The second camera captures the traffic video image on the ramp, which is denoted as the second video image , using the feature-based target vehicle tracking method to track all the vehicles within the range of the camera and heading towards the merged area to obtain the trajectories of all vehicles;

步骤三，对于第一视频图像，计算各个车辆的实际行驶速度，即在任意第x帧图像中利用Moravec算法各个车的角点，比较各角点的兴趣值，筛选兴趣值最大的点，即该区域内特征最明显的点，作为各个车辆在第x帧图像的位置；采用相同的方法，在第x+a帧图像中确定各车辆在第x+a帧图像的位置，记录图像中各个角点的位置；得到图像像素行和实际距离之间的映射关系，即映射表，根据映射表得到各个车辆在第x帧图像中的实际位置S_i(i＝1，2…,n)，在第x+a帧图像中的实际位置S_i’(i＝1，2…,n)，则有：Step 3, for the first video image, calculate the actual driving speed of each vehicle, that is, use the Moravec algorithm to compare the interest values of each corner point in any x-th frame image, and select the point with the largest interest value, that is The point with the most obvious features in this area is taken as the position of each vehicle in the x-th frame image; using the same method, determine the position of each vehicle in the x+a-th frame image in the x+a-th frame image, and record each vehicle in the image The position of the corner point; the mapping relationship between the image pixel row and the actual distance is obtained, that is, the mapping table, and the actual position S_i (i=1, 2..., n) of each vehicle in the image of the xth frame is obtained according to the mapping table, The actual position S_i '(i=1, 2...,n) in the image of frame x+a, then:

${\mathrm{<span><span>V</span>V</span>}}_{\mathrm{<span><span>i</span>i</span>}}<span><span>=</span>=</span>\frac{<span><span>(</span>(</span>{\mathrm{<span><span>S</span>S</span>}}_{\mathrm{<span><span>i</span>i</span>}}^{<span><span>,</span>,</span>}<span><span>-</span>-</span>{\mathrm{<span><span>S</span>S</span>}}_{\mathrm{<span><span>i</span>i</span>}}<span><span>)</span>)</span>{\mathrm{<span><span>V</span>V</span>}}_{\mathrm{<span><span>0</span>0</span>}}}{\mathrm{<span><span>a</span>a</span>}}<span><span>(</span>(</span>\mathrm{<span><span>i</span>i</span>}<span><span>=</span>=</span>\mathrm{<span><span>1,2</span>1,2</span>}<span><span>.</span>.</span><span><span>.</span>.</span><span><span>.</span>.</span><span><span>,</span>,</span>\mathrm{<span><span>n</span>no</span>}<span><span>)</span>)</span>$

式中：In the formula:

V_i—各个车辆的行驶速度，单位：帧/s；V_i —the driving speed of each vehicle, unit: frame/s;

V₀—视频播放速度，单位：帧/s；V₀ —Video playback speed, unit: frame/s;

a—连续跟踪的图像帧数，单位：帧；a—number of image frames for continuous tracking, unit: frame;

步骤四，对于第二视频图像，计算各个车辆的实际行驶速度，即在任意第y帧图像中利用步骤三的方法确定各个车辆在第y帧图像的位置，在第y+a帧图像确定各个车辆在第y+a帧图像的位置，记录图像中各个角点的位置；得到图像像素行和实际距离之间的映射关系，即映射表，根据映射表得到各车辆在第y帧图像中的实际位置SS_j(j＝1，2…,m)，在第y+a帧图像中的实际位置SS_j’(j＝1，2…,m)，则有：Step 4, for the second video image, calculate the actual driving speed of each vehicle, that is, use the method of step 3 in any yth frame image to determine the position of each vehicle in the yth frame image, and determine the position of each vehicle in the y+ath frame image The position of the vehicle in the y+a frame image, record the position of each corner point in the image; obtain the mapping relationship between the image pixel row and the actual distance, that is, the mapping table, according to the mapping table, obtain the position of each vehicle in the yth frame image The actual position SS_j (j=1, 2..., m), the actual position SS_j '(j=1, 2..., m) in the y+ath frame image, then:

${\mathrm{<span><span>VV</span>VV</span>}}_{\mathrm{<span><span>j</span>j</span>}}<span><span>=</span>=</span>\frac{<span><span>(</span>(</span>{\mathrm{<span><span>SS</span>SS</span>}}_{\mathrm{<span><span>j</span>j</span>}}^{<span><span>,</span>,</span>}<span><span>-</span>-</span>{\mathrm{<span><span>SS</span>SS</span>}}_{\mathrm{<span><span>j</span>j</span>}}<span><span>)</span>)</span>{\mathrm{<span><span>V</span>V</span>}}_{\mathrm{<span><span>0</span>0</span>}}}{\mathrm{<span><span>a</span>a</span>}}<span><span>(</span>(</span>\mathrm{<span><span>j</span>j</span>}<span><span>=</span>=</span>\mathrm{<span><span>1,2</span>1,2</span>}<span><span>.</span>.</span><span><span>.</span>.</span><span><span>.</span>.</span><span><span>,</span>,</span>\mathrm{<span><span>m</span>m</span>}<span><span>)</span>)</span>$

式中：In the formula:

VV_i—各个车辆的行驶速度，单位：帧/s；VV_i —the driving speed of each vehicle, unit: frame/s;

V₀—视频播放速度，单位：帧/s；V₀ —Video playback speed, unit: frame/s;

a—连续跟踪的图像帧数，单位：帧；a—number of image frames for continuous tracking, unit: frame;

步骤五，根据第一视频图像和第二视频图像中各个车辆距并入区域的实际距离以及个车辆的实际行驶速度，得到主干道任意车辆驶入并入区域与匝道上任意车辆驶入并入区域的时间差，记为t,则有：Step 5: According to the actual distance of each vehicle from the merging area in the first video image and the second video image and the actual driving speed of each vehicle, it is obtained that any vehicle on the main road enters the merging area and any vehicle on the ramp enters the merging area. The time difference of the region, denoted as t, is:

$\mathrm{<span><span>t</span>t</span>}<span><span>=</span>=</span>\frac{<span><span>(</span>(</span>{\mathrm{<span><span>L</span>L</span>}}_{\mathrm{<span><span>1</span>1</span>}}<span><span>-</span>-</span>{\mathrm{<span><span>S</span>S</span>}}_{\mathrm{<span><span>i</span>i</span>}}^{<span><span>,</span>,</span>}<span><span>)</span>)</span>}{{\mathrm{<span><span>V</span>V</span>}}_{\mathrm{<span><span>i</span>i</span>}}}<span><span>-</span>-</span>\frac{<span><span>(</span>(</span>{\mathrm{<span><span>L</span>L</span>}}_{\mathrm{<span><span>2</span>2</span>}}<span><span>-</span>-</span>{\mathrm{<span><span>SS</span>SS</span>}}_{\mathrm{<span><span>j</span>j</span>}}^{<span><span>,</span>,</span>}<span><span>)</span>)</span>}{{\mathrm{<span><span>VV</span>VV</span>}}_{\mathrm{<span><span>j</span>j</span>}}}<span><span>(</span>(</span>\mathrm{<span><span>i</span>i</span>}<span><span>=</span>=</span>\mathrm{<span><span>1,2</span>1,2</span>}<span><span>.</span>.</span><span><span>.</span>.</span><span><span>.</span>.</span><span><span>,</span>,</span>\mathrm{<span><span>n</span>no</span>}<span><span>,</span>,</span>\mathrm{<span><span>j</span>j</span>}<span><span>=</span>=</span>\mathrm{<span><span>1,2</span>1,2</span>}<span><span>.</span>.</span><span><span>.</span>.</span><span><span>.</span>.</span><span><span>,</span>,</span>\mathrm{<span><span>m</span>m</span>}<span><span>)</span>)</span>$

当|t|＞5s时，则两辆车辆进入并入区时安全；When |t|＞5s, it is safe for two vehicles to enter the merging area;

当|t|≤5s时，则两辆车辆进入并入区时可能会产生冲突，并在主干道和匝道的情报表上显示有可能发生冲突的车辆。When |t|≤5s, two vehicles may conflict when entering the merging area, and the vehicles that may conflict may be displayed on the information tables of the main road and ramp.

本发明的基于视频的高速公路匝道车辆并入安全预警方法，与现有技术相比，不受环境限制，能够对对视频范围内所有车辆进行实时、可靠的预警。并且易于实现、准确性较高，很适合于高速公路匝道的实时交通预警，具有广阔的应用前景。Compared with the prior art, the video-based safety early warning method for vehicles on the highway ramp of the present invention is not restricted by the environment, and can perform real-time and reliable early warning for all vehicles within the video range. Moreover, it is easy to implement and has high accuracy, which is very suitable for real-time traffic warning of expressway ramps and has broad application prospects.

附图说明Description of drawings

图1为高速公路匝道车辆并入示意图。Figure 1 is a schematic diagram of the merging of vehicles on an expressway ramp.

图2为高速公路主干道上各个车辆的运动轨迹的示意图。FIG. 2 is a schematic diagram of the movement trajectories of various vehicles on the main road of the expressway.

图3为高速公路匝道上各个车辆的运动轨迹的示意图。FIG. 3 is a schematic diagram of the movement trajectories of various vehicles on the expressway ramp.

图4为高速公路主干道车辆与匝道车辆冲突检测流程图。Figure 4 is a flow chart of collision detection between vehicles on the main road of the expressway and vehicles on the ramp.

图5为高速公路匝道车辆与主干道道车辆冲突检测流程图。Fig. 5 is a flow chart of collision detection between expressway ramp vehicles and arterial vehicles.

图6为高速公路主干道的情报表，该情报表显示匝道上运行的车辆。Fig. 6 is the information table of the main road of the expressway, which shows the vehicles running on the ramp.

图7为高速公路匝道的情报表，该情报表显示主干道上运行的车辆。Fig. 7 is an information table of an expressway ramp, which shows the vehicles running on the main road.

图中：黑色线条代表高速公路分道线，黑色小黑框代表可能会发生冲突的车辆，黑色椭圆形代表高速公路上运行的车辆，黑色椭圆形上的黑线代表车辆运行的轨迹。In the figure: the black line represents the lane divider of the expressway, the small black frame represents the vehicles that may conflict, the black oval represents the vehicle running on the expressway, and the black line on the black oval represents the trajectory of the vehicle.

以下结合附图和实施例对本发明的内容作进一步详细说明。The content of the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments.

具体实施方式Detailed ways

本实施例给出一种基于视频的高速公路匝道车辆并入安全预警方法，利用视频检测以及图像处理的相关技术，求出高速公路主干道和匝道上车辆的实际距离和实际行驶速度，对其到达并入区的时间进行准确计算，从而实现高速公路匝道车辆并入安全的实时预警。需要说明的是本实施例中的映射表采用发明专利“一种线性模型下的摄像机几何标定方法”（公开(公告)号:CN102222332A）中所述的摄像机几何标定方法得到。This embodiment provides a video-based method for merging safety warnings of vehicles on freeway ramps, using video detection and image processing related technologies to obtain the actual distance and actual driving speed of vehicles on the main road of the expressway and on the ramp. The time to arrive at the merging area is accurately calculated, so as to realize the real-time early warning of the merging safety of expressway ramp vehicles. It should be noted that the mapping table in this embodiment is obtained by using the camera geometric calibration method described in the invention patent "a camera geometric calibration method under linear model" (publication (publication) number: CN102222332A).

如图1至图7所示，本实施例的方法具体按下列步骤进行：As shown in Figures 1 to 7, the method of this embodiment is specifically carried out according to the following steps:

步骤一，在高速路的匝道口沿着主干道方向架设第一摄像机，第一摄像机的射程记为L₁，第一摄像机采集主干道上的交通视频图像，记为第一视频图像，采用基于特征的目标车辆跟踪方法对摄像机射程范围内的所有驶向并入区域的所有车辆进行跟踪得到所有辆车的运动轨迹；Step 1: Set up the first camera along the direction of the main road at the ramp entrance of the expressway. The range of the first camera is recorded as L₁ . The first camera collects the traffic video image on the main road, which is recorded as the first video image. The characteristic target vehicle tracking method tracks all the vehicles within the range of the camera to the merged area to obtain the movement trajectories of all vehicles;

所述的基于特征的目标车辆跟踪方法即：采用基于像素块的帧间差分法检测运动车辆，对检测出的运动图像利用Moravec算法提取相应的角点作为目标位置，并对相应的角点进行匹配跟踪；The described target vehicle tracking method based on features is: adopt the inter-frame difference method based on the pixel block to detect the moving vehicle, utilize the Moravec algorithm to extract the corresponding corner points as the target position for the detected moving images, and perform corresponding corner points match tracking;

步骤二，在高速路的匝道口第一摄像机架设处沿着匝道方向架设第二摄像机，第二摄像机的射程记为L₂，第二摄像机采集匝道上的交通视频图，记为第二视频图像，采用基于特征的目标车辆跟踪方法对摄像机射程范围内的所有驶向并入区域的所有车辆进行跟踪得到所有辆车的运动轨迹；Step 2: Set up a second camera along the direction of the ramp at the place where the first camera is installed at the ramp entrance of the expressway. The range of the second camera is denoted as L₂ . The second camera captures the traffic video image on the ramp, which is denoted as the second video image , using the feature-based target vehicle tracking method to track all the vehicles within the range of the camera and heading towards the merged area to obtain the trajectories of all vehicles;

步骤三，对于第一视频图像，计算各个车辆的实际行驶速度，即在任意第x帧图像中利用Moravec算法各个车的角点，比较各角点的兴趣值，筛选兴趣值最大的点，即该区域内特征最明显的点，作为各个车辆在第x帧图像的位置；采用相同的方法，在第x+a帧图像中确定各车辆在第x+a帧图像的位置，记录图像中各个角点的位置；得到图像像素行和实际距离之间的映射关系，即映射表，根据映射表得到各个车辆在第x帧图像中的实际位置S_i(i＝1，2…,n)，在第x+a帧图像中的实际位置S_i’(i＝1，2…,n)，则有：Step 3, for the first video image, calculate the actual driving speed of each vehicle, that is, use the Moravec algorithm to compare the interest values of each corner point in any x-th frame image, and select the point with the largest interest value, that is The point with the most obvious features in this area is taken as the position of each vehicle in the x-th frame image; using the same method, determine the position of each vehicle in the x+a-th frame image in the x+a-th frame image, and record each vehicle in the image The position of the corner point; the mapping relationship between the image pixel row and the actual distance is obtained, that is, the mapping table, and the actual position S_i (i=1, 2..., n) of each vehicle in the image of the xth frame is obtained according to the mapping table, The actual position S_i '(i=1, 2...,n) in the image of frame x+a, then:

${\mathrm{<span><span>V</span>V</span>}}_{\mathrm{<span><span>i</span>i</span>}}<span><span>=</span>=</span>\frac{<span><span>(</span>(</span>{\mathrm{<span><span>S</span>S</span>}}_{\mathrm{<span><span>i</span>i</span>}}^{<span><span>,</span>,</span>}<span><span>-</span>-</span>{\mathrm{<span><span>S</span>S</span>}}_{\mathrm{<span><span>i</span>i</span>}}<span><span>)</span>)</span>{\mathrm{<span><span>V</span>V</span>}}_{\mathrm{<span><span>0</span>0</span>}}}{\mathrm{<span><span>a</span>a</span>}}<span><span>(</span>(</span>\mathrm{<span><span>i</span>i</span>}<span><span>=</span>=</span>\mathrm{<span><span>1,2</span>1,2</span>}<span><span>.</span>.</span><span><span>.</span>.</span><span><span>.</span>.</span><span><span>,</span>,</span>\mathrm{<span><span>n</span>no</span>}<span><span>)</span>)</span>$

式中：In the formula:

V_i—各个车辆的行驶速度，单位：帧/s；V_i —the driving speed of each vehicle, unit: frame/s;

V₀—视频播放速度，单位：帧/s；V₀ —Video playback speed, unit: frame/s;

a—连续跟踪的图像帧数，单位：帧；a—number of image frames continuously tracked, unit: frame;

步骤四，对于第二视频图像，计算各个车辆的实际行驶速度，即在任意第y帧图像中利用步骤三的方法确定各个车辆在第y帧图像的位置，在第y+a帧图像确定各个车辆在第y+a帧图像的位置，记录图像中各个角点的位置；得到图像像素行和实际距离之间的映射关系，即映射表，根据映射表得到各车辆在第y帧图像中的实际位置SS_j(j＝1，2…,m)，在第y+a帧图像中的实际位置SS_j’(j＝1，2…,m)，则有：Step 4, for the second video image, calculate the actual driving speed of each vehicle, that is, use the method of step 3 in any yth frame image to determine the position of each vehicle in the yth frame image, and determine the position of each vehicle in the y+ath frame image The position of the vehicle in the y+a frame image, record the position of each corner point in the image; obtain the mapping relationship between the image pixel row and the actual distance, that is, the mapping table, according to the mapping table, obtain the position of each vehicle in the yth frame image The actual position SS_j (j=1, 2..., m), the actual position SS_j '(j=1, 2..., m) in the y+ath frame image, then:

${\mathrm{<span><span>VV</span>VV</span>}}_{\mathrm{<span><span>j</span>j</span>}}<span><span>=</span>=</span>\frac{<span><span>(</span>(</span>{\mathrm{<span><span>SS</span>SS</span>}}_{\mathrm{<span><span>j</span>j</span>}}^{<span><span>,</span>,</span>}<span><span>-</span>-</span>{\mathrm{<span><span>SS</span>SS</span>}}_{\mathrm{<span><span>j</span>j</span>}}<span><span>)</span>)</span>{\mathrm{<span><span>V</span>V</span>}}_{\mathrm{<span><span>0</span>0</span>}}}{\mathrm{<span><span>a</span>a</span>}}<span><span>(</span>(</span>\mathrm{<span><span>j</span>j</span>}<span><span>=</span>=</span>\mathrm{<span><span>1,2</span>1,2</span>}<span><span>.</span>.</span><span><span>.</span>.</span><span><span>.</span>.</span><span><span>,</span>,</span>\mathrm{<span><span>m</span>m</span>}<span><span>)</span>)</span>$

式中：In the formula:

VV_i—各个车辆的行驶速度，单位：帧/s；VV_i —the driving speed of each vehicle, unit: frame/s;

V₀—视频播放速度，单位：帧/s；V₀ —Video playback speed, unit: frame/s;

a—连续跟踪的图像帧数，单位：帧；a—number of image frames continuously tracked, unit: frame;

步骤五，根据第一视频图像和第二视频图像中各个车辆距并入区域的实际距离以及个车辆的实际行驶速度，得到主干道任意车辆驶入并入区域与匝道上任意车辆驶入并入区域的时间差，记为t,则有：Step 5: According to the actual distance of each vehicle from the merging area in the first video image and the second video image and the actual driving speed of each vehicle, it is obtained that any vehicle on the main road enters the merging area and any vehicle on the ramp enters the merging area. The time difference of the region, denoted as t, is:

$\mathrm{<span><span>t</span>t</span>}<span><span>=</span>=</span>\frac{<span><span>(</span>(</span>{\mathrm{<span><span>L</span>L</span>}}_{\mathrm{<span><span>1</span>1</span>}}<span><span>-</span>-</span>{\mathrm{<span><span>S</span>S</span>}}_{\mathrm{<span><span>i</span>i</span>}}^{<span><span>,</span>,</span>}<span><span>)</span>)</span>}{{\mathrm{<span><span>V</span>V</span>}}_{\mathrm{<span><span>i</span>i</span>}}}<span><span>-</span>-</span>\frac{<span><span>(</span>(</span>{\mathrm{<span><span>L</span>L</span>}}_{\mathrm{<span><span>2</span>2</span>}}<span><span>-</span>-</span>{\mathrm{<span><span>SS</span>SS</span>}}_{\mathrm{<span><span>j</span>j</span>}}^{<span><span>,</span>,</span>}<span><span>)</span>)</span>}{{\mathrm{<span><span>VV</span>VV</span>}}_{\mathrm{<span><span>j</span>j</span>}}}<span><span>(</span>(</span>\mathrm{<span><span>i</span>i</span>}<span><span>=</span>=</span>\mathrm{<span><span>1,2</span>1,2</span>}<span><span>.</span>.</span><span><span>.</span>.</span><span><span>.</span>.</span><span><span>,</span>,</span>\mathrm{<span><span>n</span>no</span>}<span><span>,</span>,</span>\mathrm{<span><span>j</span>j</span>}<span><span>=</span>=</span>\mathrm{<span><span>1,2</span>1,2</span>}<span><span>.</span>.</span><span><span>.</span>.</span><span><span>.</span>.</span><span><span>,</span>,</span>\mathrm{<span><span>m</span>m</span>}<span><span>)</span>)</span>$

当|t|＞5s时，则两辆车辆进入并入区时安全；When |t|＞5s, it is safe for two vehicles to enter the merging area;

当|t|≤5s时，则两辆车辆进入并入区时有产生冲突的危险，并在主干道和匝道的情报表上显示有产生冲突的危险的车辆。When |t|≤5s, there is a danger of conflict when two vehicles enter the merging area, and the information tables of the main road and the ramp will display the vehicles with the danger of conflict.

以下给出本发明的具体实施例，需要说明的是本发明并不局限于以下具体实施例，凡在本申请技术方案基础上做的等同变换均落入本发明的保护范围。Specific embodiments of the present invention are provided below, and it should be noted that the present invention is not limited to the following specific embodiments, and all equivalent transformations done on the basis of the technical solutions of the present application all fall within the scope of protection of the present invention.

实施例1：Example 1:

在高速路的匝道口沿着主干道方向架设第一摄像机，第一摄像机的射程为L₁=150米，第一摄像机采集主干道上的交通视频图像，记为第一视频图像，采用基于特征的目标车辆跟踪方法对摄像机射程范围内的所有驶向并入区域的所有车辆进行跟踪得到所有辆车的运动轨迹。在高速路的匝道口第一摄像机架设处沿着匝道方向架设第二摄像机，第二摄像机的射程记为L₂=150米，第二摄像机采集匝道上的交通视频图，记为第二视频图像，采用基于特征的目标车辆跟踪方法对摄像机射程范围内的所有驶向并入区域的所有车辆进行跟踪得到所有辆车的运动轨迹。对于第一视频图像任意选取一辆车辆，在第10帧中通过筛选选取该车辆上的一个角点作为其目标位置，在第30帧中通过筛选选取该车辆上的一个角点作为其目标位置，根据一种标记方法得到映射表，得到S₁=52米，S₁’=100米，即可确定该车辆行驶20帧的实际距离S₁’-S₁=48米，该车辆距并入区域的实际距离L₁-S₁’=50米。对于第二视频图像任意选取一辆车辆，在第20帧中通过筛选角点确定该车辆的实际位置，在第40帧中通过筛选角点确定该车辆的实际位置，根据一种标记方法得到映射表，得到SS₁=44米，SS₁’=60米，即可确定该车辆行驶20帧的实际距离SS₁’–SS₁=16米，该车辆距并入区域的实际距离L₂-SS₁’=90米。因此连续跟踪的图像帧数n=20帧，视频播放速度V₀=25帧/s。从而求出主干道车辆的速度V₁=60米/秒,匝道车辆的速度VV₁=20米/秒，主干道车辆驶入并入区域与匝道上车辆驶入并入区域的时间差|t|=3.67秒<5秒，认为主干道车辆与匝道上车辆进入并入区时有发生冲突的危险，在主干道和匝道的情报表上显示有可能发生冲突的车辆，驾驶人员根据情报板的实时信息做出判断。Set up the first camera along the direction of the main road at the ramp of the expressway. The range of the first camera is L₁ =150 meters. The first camera collects the traffic video image on the main road, which is recorded as the first video image. The target vehicle tracking method tracks all the vehicles within the range of the camera to the merged area to obtain the trajectories of all vehicles. Set up the second camera along the direction of the ramp at the place where the first camera is erected at the ramp entrance of the expressway. The range of the second camera is recorded as L₂ =150 meters. The second camera collects the traffic video image on the ramp, which is recorded as the second video image , using the feature-based target vehicle tracking method to track all the vehicles within the range of the camera and heading to the merged area to obtain the trajectories of all vehicles. For the first video image, randomly select a vehicle, select a corner point on the vehicle as its target position by screening in the 10th frame, and select a corner point on the vehicle as its target position by screening in the 30th frame , according to a marking method to obtain the mapping table, get S₁ =52 meters, S₁ '=100 meters, then determine the actual distance S₁ '-S₁ =48 meters of the vehicle driving 20 frames, the vehicle distance is incorporated into The actual distance of the area is L₁ −S₁ ′=50 meters. For the second video image, a vehicle is randomly selected, the actual position of the vehicle is determined by screening the corner points in the 20th frame, and the actual position of the vehicle is determined by screening the corner points in the 40th frame, and the mapping is obtained according to a marking method Table, get SS₁ =44 meters, SS₁ '=60 meters, you can determine the actual distance SS₁ '–SS₁ =16 meters of the vehicle driving 20 frames, the actual distance L₂ -SS between the vehicle and the merged area₁ '=90 meters. Therefore, the number of image frames for continuous tracking is n=20 frames, and the video playback speed V₀ =25 frames/s. Therefore, the speed V₁ =60 m/s of the vehicle on the main road, VV₁ =20 m/s of the vehicle on the ramp, and the time difference |t| = 3.67 seconds < 5 seconds, it is considered that there is a danger of conflict between vehicles on the main road and vehicles on the ramp when they enter the merging area. The information tables on the main road and the ramp show vehicles that may collide. information to make a judgment.

实施例2：Example 2:

在高速路的匝道口沿着主干道方向架设第一摄像机，第一摄像机的射程为L₁=150米，第一摄像机采集主干道上的交通视频图像，记为第一视频图像，采用基于特征的目标车辆跟踪方法对摄像机射程范围内的所有驶向并入区域的所有车辆进行跟踪得到所有辆车的运动轨迹。在高速路的匝道口第一摄像机架设处沿着匝道方向架设第二摄像机，第二摄像机的射程记为L₂=150米，第二摄像机采集匝道上的交通视频图，记为第二视频图像，采用基于特征的目标车辆跟踪方法对摄像机射程范围内的所有驶向并入区域的所有车辆进行跟踪得到所有辆车的运动轨迹。对于第一视频图像任意选取一辆车辆，在第10帧中通过筛选选取该车辆上的一个角点作为其目标位置，在第20帧中通过筛选选取该车辆上的一个角点作为其目标位置，根据一种标记方法得到映射表，得到S₁=90米，S₁’=110米，即可确定该车辆行驶20帧的实际距离S₁’-S₁=20米，,该车辆距并入区域的实际距离L₁-S₁’=40米。对于第二视频图像任意选取一辆车辆，在第20帧中通过筛选角点确定该车辆的实际位置，在第30帧中通过筛选角点确定该车辆的实际位置，根据一种标记方法得到映射表，得到SS₁=54米，SS₁’=60米，即可确定该车辆行驶20帧的实际距离SS₁’–SS₁=6米，该车辆距并入区域的实际距离L₂-SS₁’=90米。因此连续跟踪的图像帧数n=10帧，视频播放速度V₀=25帧/s。从而求出主干道车辆的速度V₁=50米/秒,匝道车辆的速度VV₁=15米/秒，主干道车辆驶入并入区域与匝道上车辆驶入并入区域的时间差|t|=5.2秒>5秒，认为主干道该车辆与匝道该车辆不会发生冲突，两辆车进入并入区时安全。Set up the first camera along the direction of the main road at the ramp of the expressway. The range of the first camera is L₁ =150 meters. The first camera collects the traffic video image on the main road, which is recorded as the first video image. The target vehicle tracking method tracks all the vehicles within the range of the camera to the merged area to obtain the trajectories of all vehicles. Set up the second camera along the direction of the ramp at the place where the first camera is erected at the ramp entrance of the expressway. The range of the second camera is recorded as L₂ =150 meters. The second camera collects the traffic video image on the ramp, which is recorded as the second video image , using the feature-based target vehicle tracking method to track all the vehicles within the range of the camera and heading to the merged area to obtain the trajectories of all vehicles. For the first video image, randomly select a vehicle, select a corner point on the vehicle as its target position by screening in the 10th frame, and select a corner point on the vehicle as its target position by screening in the 20th frame , according to a marking method to obtain the mapping table, get S₁ =90 meters, S₁ '=110 meters, then you can determine the actual distance S₁ '-S₁ =20 meters of the vehicle driving 20 frames, the distance between the vehicle and The actual distance L₁ -S₁ '=40 meters into the area. For the second video image, a vehicle is randomly selected, the actual position of the vehicle is determined by screening the corner points in the 20th frame, and the actual position of the vehicle is determined by screening the corner points in the 30th frame, and the mapping is obtained according to a marking method Table, get SS₁ =54 meters, SS₁ '=60 meters, you can determine the actual distance SS₁ '–SS₁ =6 meters of the vehicle driving 20 frames, and the actual distance L₂ -SS between the vehicle and the merged area₁ '=90 meters. Therefore, the number of image frames for continuous tracking is n=10 frames, and the video playback speed V₀ =25 frames/s. Therefore, the speed V₁ =50 m/s of the vehicle on the main road, VV₁ =15 m/s of the vehicle on the ramp, and the time difference |t| =5.2 seconds>5 seconds, it is considered that the vehicle on the main road will not conflict with the vehicle on the ramp, and it is safe when the two vehicles enter the merge area.

Claims (1)

1. the expressway ramp vehicle based on video is incorporated to a safe early warning method, it is characterized in that, the method is carried out according to following steps:

Step one, major trunk roads direction in the ring road opening's edge of expressway and set up the first video camera, the range of the first video camera is designated as L1, traffic video image on first camera acquisition major trunk roads, be designated as the first video image, adopt the target vehicle tracking of feature based to follow the tracks of to all vehicles being incorporated to region that drive towards in video camera range region the movement locus obtaining all cars;

The target vehicle tracking of described feature based is namely: adopt the frame differential method based on block of pixels to detect moving vehicle, utilize Moravec algorithm to extract corresponding angle point as target location to the moving image detected, and matched jamming is carried out to corresponding angle point;

Step 2, set up the second video camera in ring road mouth first video camera erection place of expressway along ring road direction, the range of the second video camera is designated as L₂, the traffic video figure on the second camera acquisition ring road, is designated as the second video image, adopts the target vehicle tracking of feature based to follow the tracks of to all vehicles being incorporated to region that drive towards in video camera range region the movement locus obtaining all cars;

Step 3, for the first video image, calculate the actual travel speed of each vehicle, namely in any xth two field picture, Moravec algorithm is utilized to calculate the angle point of each car, the interest value of more each angle point, the point that screening interest value is maximum, i.e. the most obvious point of feature in this region, as the position of each vehicle at xth two field picture; Adopt identical method, in xth+a two field picture, determine the position of each vehicle at xth+a two field picture, the position of each angle point in record image; Obtain the mapping relations between the capable and actual range of image pixel, i.e. mapping table, obtains the physical location S of each vehicle in xth two field picture according to mapping table_i(i=1,2 ..., n), the physical location S ' in xth+a two field picture_i(i=1,2 ..., n), then have:

$V_i=\frac{(S_i^{,}-S_i)V_0}{a}(i=\mathrm{1,2}...,n)$

In formula:

V_ithe travel speed of-each vehicle, unit: frame/s;

V₀-video playout speed, unit: frame/s;

The number of image frames of a-Continuous Tracking, unit: frame;

Step 4, for the second video image, calculate the actual travel speed of each vehicle, namely in any y two field picture, utilize the method for step 3 to determine that each vehicle is in the position of y two field picture, determine that each vehicle is in the position of y+a two field picture at y+a two field picture, the position of each angle point in record image; Obtain the mapping relations between the capable and actual range of image pixel, i.e. mapping table, obtains the physical location SS of each vehicle in y two field picture according to mapping table_j(j=1,2 ..., m), the physical location SS ' in y+a two field picture_j(j=1,2 ..., m), then have:

${\mathrm{VV}}_j\frac{({\mathrm{SS}}_j^{,}-{\mathrm{SS}}_j)V_0}{a}(j=\mathrm{1,2}...,m)$

In formula:

VV_jthe travel speed of-each vehicle, unit: frame/s;

V₀-video playout speed, unit: frame/s;

The number of image frames of a-Continuous Tracking, unit: frame;

Step 5, according to each vehicle in the first video image and the second video image apart from being incorporated to the actual range in region and the actual travel speed of each vehicle, obtain any vehicle of major trunk roads to sail into and be incorporated to any vehicle on region and ring road and sail the mistiming being incorporated to region into, be designated as t, then have:

$t=\frac{(L_1-S_i^{,})}{V_i}-\frac{(L_2-{\mathrm{SS}}_j^{,})}{{\mathrm{VV}}_j}(i=\mathrm{1,2}...,n,j=\mathrm{1,2}...,m)$

When | during t| > 5s, then two vehicles enters safety when being incorporated to district;

When | during t|≤5s, then two vehicles enters when being incorporated to district and may produce conflict, and shows the vehicle that may clash on the information table of major trunk roads and ring road.