Video-based Person Re-identification with Accumulative Motion Context
arXiv:1701.00193
Abstract
Video based person re-identification plays a central role in realistic security and video surveillance. In this paper we propose a novel Accumulative Motion Context (AMOC) network for addressing this important problem, which effectively exploits the long-range motion context for robustly identifying the same person under challenging conditions. Given a video sequence of the same or different persons, the proposed AMOC network jointly learns appearance representation and motion context from a collection of adjacent frames using a two-stream convolutional architecture. Then AMOC accumulates clues from motion context by recurrent aggregation, allowing effective information flow among adjacent frames and capturing dynamic gist of the persons. The architecture of AMOC is end-to-end trainable and thus motion context can be adapted to complement appearance clues under unfavorable conditions (e.g. occlusions). Extensive experiments are conduced on three public benchmark datasets, i.e., the iLIDS-VID, PRID-2011 and MARS datasets, to investigate the performance of AMOC. The experimental results demonstrate that the proposed AMOC network outperforms state-of-the-arts for video-based re-identification significantly and confirm the advantage of exploiting long-range motion context for video based person re-identification, validating our motivation evidently.
accepted by TCSVT
References in corpus (4)
Cited by in corpus (5)
- AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
- Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification
- Neural Person Search Machines
- Three-Stream Convolutional Networks for Video-based Person Re-Identification
- Learning adaptively from the unknown for few-example video person re-ID