Cross-Camera Pedestrian Trajectory Retrieval Based on Linear Trajectory Manifolds.

Abstract

The goal of pedestrian trajectory retrieval is to infer the multi-camera path of a targeted pedestrian using images or videos from a camera network, which is crucial for passenger flow analytics and individual pedestrian retrieval. Conventional approaches hinge on spatiotemporal modeling, necessitating the gathering of positional information for each camera and trajectory data between every camera pair for the training phase. To mitigate these stringent requirements, our proposed methodology employs solely temporal information for modeling. Specifically, we introduce an Implicit Trajectory Encoding scheme, dubbed Temporal Rotary Position Embedding (T-RoPE), which integrates the temporal aspects of within-camera tracklets directly into their visual representations, thereby shaping a novel feature space. Our analysis reveals that, within this refined feature space, the challenge of inter-camera trajectory extraction can be effectively addressed by delineating a linear trajectory manifold. The visual characteristics gleaned from each candidate trajectory are utilized to compare and rank against the query feature, culminating in the ultimate trajectory retrieval outcome. To validate our method, we collected a new pedestrian trajectory dataset from a multi-storey shopping mall, namely the Mall Trajectory Dataset. Extensive experimentation across diverse datasets has demonstrated the versatility of our T-RoPE module as a plug-and-play enhancement to various network architectures, significantly enhancing the precision of pedestrian trajectory retrieval tasks. The dataset and code are released at https://github.com/zhangxin1995/MTD.