Usage of Vehicle Re-Identification Models for Improved Persistent Multiple Object Tracking in Wide Area Motion Imagery

Abstract

Persistent multiple object tracking in Wide Area Motion Imagery (WAMI) is fundamental for a wide range of applications, e.g. surveillance of borders. Though impressive tracking results have been achieved by combining appearance based and motion based object detection as input for modern tracking-by-detection methods, the number of identityswitches (ID-switches) in case of many slow or stopped vehicles is still high. Instead of extracting features from the appearance based object detection model for data association between object detections and existing tracks, we propose to use visual appearance descriptors learned from reidentification tasks. For this purpose, we employed the recent re-identification model OSNet and created an aerial vehicle re-identification dataset based on publicly available aerial object tracking datasets. By applying the optimized data association scheme, we outperform state-of-the-art trackers, as the number of ID-switches is considerably reduced.

Construction and study of an improved PP-Human multi-objective tracking method

“…Multi-objective tracking is an important research direction in the field of computer vision, and it plays a key role in many practical applications [1]. It is critical to improving performance in many areas, including video surveillance, driverless cars, and smart safety systems [2].In practice, multi-target tracking faces many challenges and problems in accuracy, robustness and real-time due to inter-target occlusion, illumination change, background clutter interference, interaction and detection error [3]. These factors present additional challenges for multiobjective tracking.…”