  Video-based person Re-Identification (V-ReID) aims to retrieve specific
persons from raw videos captured by non-overlapped cameras. As a fundamental
task, it spreads many multimedia and computer vision applications. However, due
to the variations of persons and scenes, there are still many obstacles that
must be overcome for high performance. In this work, we notice that both the
long-term and short-term information of persons are important for robust video
representations. Thus, we propose a novel deep learning framework named Long
Short-Term Representation Learning (LSTRL) for effective V-ReID. More
specifically, to extract long-term representations, we propose a
Multi-granularity Appearance Extractor (MAE), in which four granularity
appearances are effectively captured across multiple frames. Meanwhile, to
extract short-term representations, we propose a Bi-direction Motion Estimator
(BME), in which reciprocal motion information is efficiently extracted from
consecutive frames. The MAE and BME are plug-and-play and can be easily
inserted into existing networks for efficient feature learning. As a result,
they significantly improve the feature representation ability for V-ReID.
Extensive experiments on three widely used benchmarks show that our proposed
approach can deliver better performances than most state-of-the-arts.
