We develop an interactive likelihood (ILH) for sequential Monte Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the pe...

Multi-target tracking (MTT) is an active and challenging research topic. Many different approaches to MTT problem exist, yet there are still few satisfactory methods of solving multi-target occlusion problem, which often appears in multi-target tracking task. The application of multi cameras in most existing researches for multi-target occlusion requires camera calibration parameters in advance...

In multi-target visual tracking, tracking failure due to missassociation can often arise from the presence of occlusions between targets. To cope with this problem, we propose the predictive estimation method that iterates occlusion prediction and occlusion status update using occlusion activity detection by utilizing joint probabilistic data association filter in order to track each target bef...

Abstract In this paper, we use the deep sort multi-target tracking algorithm to achieve of pedestrians and vehicles in traffic scenes. firstly, yolov4 is used train pedestrian vehicle detection model Then, according frame predicted by yolov4, carried out for specific targets. The uses Sort, which can be combined with less ID switching real-time reasoning deal loss occlusion, so as more stable e...