In this paper, multiple-object vehicle tracking system by affinity matching using min-cost
linear cost assignment is proposed. This tracking system is targeted to scene recordings …

Multi-object tracking is a significant field in computer vision since it provides essential
information for video surveillance and analysis. Several different deep learning-based …

Multi object tracking (MOT) is a key research technology in the environment sensing system
of automatic driving, which is very important to driving safety. Online multi object tracking …

Online multi-object tracking (MOT) has broad applications in time-critical video analysis
scenarios such as advanced driver-assistance systems (ADASs) and autonomous driving. In …

This paper presents a multi-vehicle tracking algorithm using appearance feature and motion
history based on heterogeneous deep learning aimed at autonomous driving applications …

Multi-object tracking (MOT) becomes an attractive topic due to its wide range of usability in
video surveillance and traffic monitoring. Recent improvements on MOT has focused on …

Abstract Deep learning based Multiple Object Tracking (MOT) currently relies on off-the-shelf
detectors for tracking-by-detection. This results in deep models that are detector biased and …

In this paper, we present a multiple-object vehicle tracking system. We introduce a method to
combine bounding boxes extracted from multiple CNNs-based detections as alight and …

In this work, we address the problem of multi-vehicle detection and tracking for traffic
monitoring applications. We preset a novel intelligent visual sensor for tracking-by-detection …

This paper introduces geometry and object shape and pose costs for multi-object tracking in
urban driving scenarios. Using images from a monocular camera alone, we devise pairwise …