Deep learning has been proved effective in multiple object tracking, which confronts the difficulties of frequent occlusions, confusing appearance, in‐and‐out objects, and lack of enough labelled data. Recently, deep learning based multi‐object tracking methods make a rapid progress from representation learning to network modelling due to the development of deep learning theory and benchmark setup. In this study, the authors summarise and analyse deep learning based multi‐object tracking methods which are top‐ranked in the public benchmark test. First, they investigate functionality of deep networks in these methods, and classify the methods into three categories as description enhancement using deep features, deep network embedding, and end‐to‐end deep network construction. Second, they review deep network structures in these methods, and detail the usage and training of these networks for multi‐object tracking problem. Through experimental comparison of tracking results in the benchmarks in total and by group, they finally show the effectiveness of deep networks for tracking employed in different manners, and compare the advantages of these networks and their robustness under different tracking conditions. Moreover, they analyse the limitations of current methods, and draw some useful conclusions to facilitate the exploration of new directions for multi‐object tracking.

Tracking multiple moving targets in a video is a challenge because of several factors, including noisy video data, varying number of targets, and mutual occlusion problems. The Gaussian mixture probability hypothesis density (GM-PHD) filter, which aims to recursively propagate the intensity associated with the multi-target posterior density, can overcome the difficulty caused by the data association. This paper develops a multi-target visual tracking system that combines the GM-PHD filter with object detection. First, a new birth intensity estimation algorithm based on entropy distribution and coverage rate is proposed to automatically and accurately track the newborn targets in a noisy video. Then, a robust game-theoretical mutual occlusion handling algorithm with an improved spatial color appearance model is proposed to effectively track the targets in mutual occlusion. The spatial color appearance model is improved by incorporating interferences of other targets within the occlusion region. Finally, the experiments conducted on publicly available videos demonstrate the good performance of the proposed visual tracking system.

Robot-assisted therapy (RAT) offers potential advantages for improving the social skills of children with autism spectrum disorders (ASDs). This article provides an overview of the developed technology and clinical results of the EC-FP7-funded Development of Robot-Enhanced therapy for children with AutisM spectrum disorders (DREAM) project, which aims to develop the next level of RAT in both clinical and technological perspectives, commonly referred to as robot-enhanced therapy (RET). Within this project, a supervised autonomous robotic system is collaboratively developed by an interdisciplinary consortium including psychotherapists, cognitive scientists, roboticists, computer scientists, and ethicists, which allows robot control to exceed classical remote control methods, e.g., Wizard of Oz (WoZ), while ensuring safe and ethical robot behavior. Rigorous clinical studies are conducted to validate the efficacy of RET. Current results indicate that RET can obtain an equivalent performance compared to that of human standard therapy for children with ASDs. We also discuss the next steps of developing RET robotic systems.