2017

Video-based Person Re-identification with Accumulative Motion Context

Liu, Hao, Jie, Zequn, Jayashree, Karlekar et al.

Understand

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.

Built on

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