Memory Based Online Learning of Deep Representations from Video Streams

We present a novel online unsupervised method for face identity learning from video streams. The method exploits deep face descriptors together with a memory based learning mechanism that takes advantage of the temporal coherence of visual data. Specifically, we introduce a discriminative feature matching solution based on Reverse Nearest Neighbour and a feature forgetting strategy that detect redundant features and discard them appropriately while time progresses. It is shown that the proposed learning procedure is asymptotically stable and can be effectively used in relevant applications like multiple face identification and tracking from unconstrained video streams. Experimental results show that the proposed method achieves comparable results in the task of multiple face tracking and better performance in face identification with offline approaches exploiting future information. Code will be publicly available.Comment: arXiv admin note: text overlap with arXiv:1708.0361

Learning Longterm Representations for Person Re-Identification Using Radio Signals

Person Re-Identification (ReID) aims to recognize a person-of-interest across different places and times. Existing ReID methods rely on images or videos collected using RGB cameras. They extract appearance features like clothes, shoes, hair, etc. Such features, however, can change drastically from one day to the next, leading to inability to identify people over extended time periods. In this paper, we introduce RF-ReID, a novel approach that harnesses radio frequency (RF) signals for longterm person ReID. RF signals traverse clothes and reflect off the human body; thus they can be used to extract more persistent human-identifying features like body size and shape. We evaluate the performance of RF-ReID on longitudinal datasets that span days and weeks, where the person may wear different clothes across days. Our experiments demonstrate that RF-ReID outperforms state-of-the-art RGB-based ReID approaches for long term person ReID. Our results also reveal two interesting features: First since RF signals work in the presence of occlusions and poor lighting, RF-ReID allows for person ReID in such scenarios. Second, unlike photos and videos which reveal personal and private information, RF signals are more privacy-preserving, and hence can help extend person ReID to privacy-concerned domains, like healthcare.Comment: CVPR 2020. The first three authors contributed equally to this pape

VID-Trans-ReID: Enhanced Video Transformers for Person Re-identification

Video-based person Re-identification (Re-ID) has received increasing attention recently due to its important role within surveillance video analysis. Video-based Re- ID expands upon earlier image-based methods by extracting person features temporally across multiple video image frames. The key challenge within person Re-ID is extracting a robust feature representation that is invariant to the challenges of pose and illumination variation across multiple camera viewpoints. Whilst most contemporary methods use a CNN based methodology, recent advances in vision transformer (ViT) architectures boost fine-grained feature discrimination via the use of both multi-head attention without any loss of feature robustness. To specifically enable ViT architectures to effectively address the challenges of video person Re-ID, we propose two novel modules constructs, Temporal Clip Shift and Shuffled (TCSS) and Video Patch Part Feature (VPPF), that boost the robustness of the resultant Re-ID feature representation. Furthermore, we combine our proposed approach with current best practices spanning both image and video based Re-ID including camera view embedding. Our proposed approach outperforms existing state-of-the-art work on the MARS, PRID2011, and iLIDS-VID Re-ID benchmark datasets achieving 96.36%, 96.63%, 94.67% rank-1 accuracy respectively and achieving 90.25% mAP on MARS

Enhancing vehicle re-identification via synthetic training datasets and re-ranking based on video-clips information

Vehicle re-identification (ReID) aims to find a specific vehicle identity across multiple non-overlapping cameras. The main challenge of this task is the large intra-class and small inter-class variability of vehicles appearance, sometimes related with large viewpoint variations, illumination changes or different camera resolutions. To tackle these problems, we proposed a vehicle ReID system based on ensembling deep learning features and adding different post-processing techniques. In this paper, we improve that proposal by: incorporating large-scale synthetic datasets in the training step; performing an exhaustive ablation study showing and analyzing the influence of synthetic content in ReID datasets, in particular CityFlow-ReID and VeRi-776; and extending post-processing by including different approaches to the use of gallery video-clips of the target vehicles in the re-ranking step. Additionally, we present an evaluation framework in order to evaluate CityFlow-ReID: as this dataset has not public ground truth annotations, AI City Challenge provided an on-line evaluation service which is no more available; our evaluation framework allows researchers to keep on evaluating the performance of their systems in the CityFlow-ReID datasetOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Natur