Abstract
This paper mainly establishes a large-scale Long sequence Video database for person re-IDentification (LVreID). Different from existing datasets, LVreID presents many important new features. (1) long sequences: the average sequence length is 200 frames, which convey more abundant cues like pose and viewpoint changes that can be explored for feature learning. (2) complex lighting, scene, and background variations: it is captured by 15 cameras located in both indoor and outdoor scenes in 12 time slots. (3) currently the largest size: it contains 3,772 identities and about 3 million bounding boxes. Those unique features in LVreID define a more challenging and realistic person ReID task. Spatial Aligned Temporal Pyramid Pooling (SATPP) network is proposed as a baseline algorithm to leverage the rich visual-temporal cues in LVreID for feature learning. SATPP jointly handles the misalignment issues in detected bounding boxes and efficiently aggregates the discriminative cues embedded in sequential video frames. Extensive experiments show feature extracted by SATPP outperforms several widely used video features. Our experiments also prove the ReID accuracy increases substantially along with longer sequence length. This demonstrates the advantage and necessity of using longer video sequences for person ReID.
Abstract (translated)
URL
https://arxiv.org/abs/1712.07286
