Re-Identification for Improved People Tracking

Re-identification is usually defined as the problem of deciding whether a person currently in the field of view of a camera has been seen earlier either by that camera or another. However, a different version of the problem arises even when people are seen by multiple cameras with overlapping fields of view. Current tracking algorithms can easily get confused when people come close to each other and merge trajectory fragments into trajectories that include erroneous identity switches. Preventing this means re-identifying people across trajectory fragments. In this chapter, we show that this can be done very effectively by formulating the problem as a minimum-cost maximum-flow linear program. This version of the re-identification problem can be solved in real-time and produces trajectories without identity switches. We demonstrate the power of our approach both in single- and multi-camera setups to track pedestrians, soccer players, and basketball players

Recovering people tracking errors using enhanced covariance-based signatures

International audienceThis paper presents a new approach for tracking multiple persons in a single camera. This approach focuses on re- covering tracked individuals that have been lost and are detected again, after being miss-detected (e.g. occluded) or after leaving the scene and coming back. In order to correct tracking errors, a multi-cameras re-identification method is adapted, with a real-time constraint. The proposed ap- proach uses a highly discriminative human signature based on covariance matrix, improved using background subtrac- tion, and a people detection confidence. The problem of linking several tracklets belonging to the same individual is also handled as a ranking problem using a learned pa- rameter. The objective is to create clusters of tracklets de- scribing the same individual. The evaluation is performed on PETS2009 dataset showing promising results

Learning Representations for Human Identification

Long-duration visual tracking of people requires the ability to link track snippets (a.k.a. tracklets) based on the identity of people. In lack of the availability of motion priors or hard biometrics (e.g., face, fingerprint, or iris), the common practice is to leverage soft biometrics for matching tracklets corresponding to the same person in different sightings. A common choice is to use the whole-body visual appearance of the person, as determined by the clothing, which is assumed to not change during tracking. The problem is challenging because distinct images of the same person may look very different, since no restrictions are imposed on the nuisance factors of variation, such as pose, illumination, viewpoint, background, and sensor noise, leading to very high intra-class variances, which make this human identification task still prone to high mismatch rates. We introduce and study models for learning representations for human identification that aim at reducing the effects of nuisance factors. First, we introduce a modeling framework based on learning a low rank representation, which can be applied to face as well as whole-body images. The goal is to not only learn invariant representations for each identity, but also to promote a uniform inter-class separation to further reduce mismatch rates. Another advantage of the approach is a fast procedure for computing and comparing invariant representations for recognition and re-identification. Second, we introduce a learning framework for fusing representations of multiple biometrics for human identification. We focus on the face modality and clothing appearance and develop a representation fusion approach based on the Information Bottleneck method. In the last part of the dissertation, we improve person re-identification by decreasing the effects of nuisance factors via multi-task learning. We design and combine improved versions of classification and distance metric losses. Classification losses improve their performance by imposing restrictions on the computation of their outputs. This makes their training harder. We mitigate this by investigating the combination of multiple tasks, such as attribute and metric learning, that might regularize the training while improving performance. Finally, we also include the explicit modeling of nuisance factors such as pose, to further improve the invariance of representations. For each model, we show the benefits of the proposed methods by characterizing their performance based on publicly available benchmarks, and by comparing them with the state of the art

A bank of unscented Kalman filters for multimodal human perception with mobile service robots

A new generation of mobile service robots could be ready soon to operate in human environments if they can robustly estimate position and identity of surrounding people. Researchers in this field face a number of challenging problems, among which sensor uncertainties and real-time constraints. In this paper, we propose a novel and efficient solution for simultaneous tracking and recognition of people within the observation range of a mobile robot. Multisensor techniques for legs and face detection are fused in a robust probabilistic framework to height, clothes and face recognition algorithms. The system is based on an efficient bank of Unscented Kalman Filters that keeps a multi-hypothesis estimate of the person being tracked, including the case where the latter is unknown to the robot. Several experiments with real mobile robots are presented to validate the proposed approach. They show that our solutions can improve the robot's perception and recognition of humans, providing a useful contribution for the future application of service robotics