Tracking of moving vehicles and pedestrians is the most important application in traffic surveillance videos. This study develops a highly efficient and fast multi-object tracking method using three-frame differencing-combined-background subtraction (TFDCBS)-coupled-automatic and fast histogram-entropy-based thresholding (HEBT) method together with GMPFM-GMPHD filters and VGG16-LSTM classifier. Here TFDCBS-HEBT methods identify the targeted objects with enclosed 3D bounding boxes and extracts multiple features from the raw images. Maximum number of error-free extracted multiple features (key points, multiple local convolutions, corners, and descriptors) are processed subsequently for object tracking by GMPFM-GMPHD Filters and an upgraded VGG16- LSTM classifier. The proposed method has been validated on KITTI 3D bounding box-dataset and its performance compared with three state-of-the-art tracking methods. Highest values of several performance parameters and the lowest computation time clearly demonstrate the promising feature of our new method for its application towards a fast and effective multi-target tracking of moving objects.

跟踪行驶中的车辆和行人是交通监控视频中最重要的应用。这项研究使用三帧差分组合背景减法（TFDCBS）耦合自动和基于直方图熵的快速阈值（HEBT）方法以及GMPFM-GMPHD滤波器和VGG16开发了一种高效且快速的多目标跟踪方法-LSTM分类器。在此，TFDCBS-HEBT方法使用封闭的3D边界框识别目标对象，并从原始图像中提取多个特征。随后，由GMPFM-GMPHD过滤器和升级的VGG16-LSTM分类器处理最大数量的无错误提取的多个特征（关键点，多个局部卷积，角和描述符），以进行对象跟踪。与三种最新的跟踪方法相比，该方法已在KITTI 3D边界框数据集及其性能上得到了验证。几个性能参数的最大值和最短的计算时间清楚地证明了我们的新方法在快速，有效地对运动对象进行多目标跟踪中的应用前景广阔。