Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers

Online Multi-Object Tracking (MOT) from videos is a challenging computer vision task which has been extensively studied for decades. Most of the existing MOT algorithms are based on the Tracking-by-Detection (TBD) paradigm combined with popular machine learning approaches which largely reduce the human effort to tune algorithm parameters. However, the commonly used supervised learning approaches require the labeled data (e.g., bounding boxes), which is expensive for videos. Also, the TBD framework is usually suboptimal since it is not end-to-end, i.e., it considers the task as detection and tracking, but not jointly. To achieve both label-free and end-to-end learning of MOT, we propose a Tracking-by-Animation framework, where a differentiable neural model first tracks objects from input frames and then animates these objects into reconstructed frames. Learning is then driven by the reconstruction error through backpropagation. We further propose a Reprioritized Attentive Tracking to improve the robustness of data association. Experiments conducted on both synthetic and real video datasets show the potential of the proposed model. Our project page is publicly available at: https://github.com/zhen-he/tracking-by-animationComment: CVPR 201

Human detection and tracking through temporal feature recognition

The ability to accurately track objects of interest – particularly humans – is of great importance in the fields of security and surveillance. In such scenarios, t he application of accurate, automated human tracking offers benefits over manual supervision. In this paper, recent efforts made to investigate the improvement of automated human detection and tracking techniques through the recognition of person-specific time-varying signatures in thermal video are detailed. A robust human detection algorithm is developed to aid the initialisation stage of a state-of-the art existing tracking algorithm. In addition, coupled with the spatial tracking methods present in this algorithm, the inclusion of temporal signature recognition in the tracking process is shown to improve human tracking results

Multisensor-based human detection and tracking for mobile service robots

The one of fundamental issues for service robots is human-robot interaction. In order to perform such a task and provide the desired services, these robots need to detect and track people in the surroundings. In the present paper, we propose a solution for human tracking with a mobile robot that implements multisensor data fusion techniques. The system utilizes a new algorithm for laser-based legs detection using the on-board LRF. The approach is based on the recognition of typical leg patterns extracted from laser scans, which are shown to be very discriminative also in cluttered environments. These patterns can be used to localize both static and walking persons, even when the robot moves. Furthermore, faces are detected using the robot's camera and the information is fused to the legs position using a sequential implementation of Unscented Kalman Filter. The proposed solution is feasible for service robots with a similar device configuration and has been successfully implemented on two different mobile platforms. Several experiments illustrate the effectiveness of our approach, showing that robust human tracking can be performed within complex indoor environments

Human detection and tracking via Ultra-Wideband (UWB) radar

This paper presents an algorithm for human presence detection and tracking using an Ultra-Wideband (UWB) impulse-based mono-static radar. UWB radar can complement other human tracking technologies, as it works well in poor visibility conditions. UWB electromagnetic wave scattering from moving humans forms a complex returned signal structure which can be approximated to a specular multi-path scattering model (SMPM). The key technical challenge is to simultaneously track multiple humans (and non-humans) using the complex scattered waveform observations. We develop a multiple-hypothesis tracking (MHT) framework that solves the complicated data association and tracking problem for an SMPM of moving objects/targets. Human presence detection utilizes SMPM signal features, which are tested in a classical likelihood ratio (LR) detector framework. The process of human detection and tracking is a combination of the MHT method and the LR human detector. We present experimental results in which a mono-static UWB radar tracks human and non-human targets, and detects human presence by discerning human from moving non-human objects

Pedestrian Detection and Tracking in Video Surveillance System: Issues, Comprehensive Review, and Challenges

Pedestrian detection and monitoring in a surveillance system are critical for numerous utility areas which encompass unusual event detection, human gait, congestion or crowded vicinity evaluation, gender classification, fall detection in elderly humans, etc. Researchers’ primary focus is to develop surveillance system that can work in a dynamic environment, but there are major issues and challenges involved in designing such systems. These challenges occur at three different levels of pedestrian detection, viz. video acquisition, human detection, and its tracking. The challenges in acquiring video are, viz. illumination variation, abrupt motion, complex background, shadows, object deformation, etc. Human detection and tracking challenges are varied poses, occlusion, crowd density area tracking, etc. These results in lower recognition rate. A brief summary of surveillance system along with comparisons of pedestrian detection and tracking technique in video surveillance is presented in this chapter. The publicly available pedestrian benchmark databases as well as the future research directions on pedestrian detection have also been discussed