Multiple Fisheye Camera Tracking via Real-Time Feature Clustering

Abstract

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.