To exploit rich information from unlabeled data, in this work, we propose a novel self-supervised framework for visual tracking which can easily adapt the state-of-the-art supervised Siamese-based trackers into unsupervised ones by utilizing the fact that an image and any cropped region of it can form a natural pair for self-training. Besides common geometric transformation-based data augmentation and hard negative mining, we also propose adversarial masking which helps the tracker to learn other context information by adaptively blacking out salient regions of the target. The proposed approach can be trained offline using images only without any requirement of manual annotations and temporal information from multiple consecutive frames. Thus, it can be used with any kind of unlabeled data, including images and video frames. For evaluation, we take SiamFC as the base tracker and name the proposed self-supervised method as S2SiamFC. Extensive experiments and ablation studies on the challenging VOT2016 and VOT2018 datasets are provided to demonstrate the effectiveness of the proposed method which not only achieves comparable performance to its supervised counterpart and other unsupervised methods requiring multiple frames.

Recently, Multi-Target Multi-Camera Tracking (MTMC) makes a breakthrough due to the release of DukeMTMC and show the feasibility of related applications. However, most of the existing MTMC methods focus on the batch methods which attempt to find the global optimal solution from the entire image sequence and thus are not suitable for the real-time applications, e.g., customer tracking in unmanned stores. In this paper, we propose a low-cost online tracking algorithm, namely, Deep Multi-Fisheye-Camera Tracking (DeepMFCT) to identify the customers and locate the corresponding positions from multiple overlapping fisheye cameras. Based on any single camera tracking algorithm (e.g., Deep SORT), our proposed algorithm establishes the correlation between different single camera tracks. Owing to the lack of well-annotated multiple overlapping fisheye cameras dataset, the main challenge of this issue is to efficiently overcome the domain gap problem between normal cameras and fisheye cameras based on existed deep learning based model. To address this challenge, we integrate a single camera tracking algorithm with cross camera clustering including location information that achieves great performance on the unmanned store dataset and Hall dataset. Experimental results show that the proposed algorithm improves the baselines by at least 7% in terms of MOTA on the Hall dataset.