Real-time multichannel video analysis is significant for intelligent transportation. Considering that deep learning and correlation filter (CF) tracking are time-consuming, a vehicle tracking method for traffic scenes is presented based on a detection-based tracking (DBT) framework. To design the model of vehicle detection, the You Only Look Once (YOLO) model is used, and then, two constraints including object attribute information and intersection over union (IOU), are combined to modify the vehicle detection box. This approach improves vehicle detection precision. In the design of tracking model, a lightweight feature extraction network model for vehicle tracking is constructed. An inception module is used in this model to reduce the computational load and increase the adaptivity of the network scale. And a squeeze-and-excitation channel attention mechanism is adopted to enhance feature learning. Regarding the object tracking strategy, the method of combining a spatial constraint and filter template matching is adopted. The observation value and prediction value are matched and corrected to achieve stable tracking of the target. Based on the interference of occlusion in target tracking, the spatial position, moving direction, and historical feature correlation of the target are comprehensively employed to achieve continuous tracking of the target.

中文翻译：

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结合检测器和跟踪器的车辆跟踪算法

实时多通道视频分析对于智能交通至关重要。考虑到深度学习和相关过滤（CF）跟踪很费时，基于基于检测的跟踪（DBT）框架提出了一种交通场景的车辆跟踪方法。为了设计车辆检测模型，使用了“仅看一次”（YOLO）模型，然后结合了两个约束（包括对象属性信息和交叉交叉（IOU））来修改车辆检测框。该方法提高了车辆检测精度。在跟踪模型的设计中，构建了用于车辆跟踪的轻量级特征提取网络模型。在该模型中使用了一个初始模块来减少计算量并增加网络规模的适应性。并采用了挤压激励通道注意机制来增强特征学习。关于对象跟踪策略，采用了空间约束和过滤器模板匹配相结合的方法。观测值和预测值进行匹配和校正，以实现对目标的稳定跟踪。基于遮挡物对目标跟踪的干扰，综合利用目标物的空间位置，运动方向和历史特征相关性，实现对目标物的连续跟踪。