SSN3D: Self-Separated Network to Align Parts for 3D Convolution in Video Person Re-Identification

Xiaoke Jiang, Yu Qiao, Junjie Yan, qichen li, Wanrong Zheng, Dapeng Chen

Abstract:

Temporal appearance misalignment is a crucial problem in
video person re-identification. The same part of person (e.g.
head or hand) appearing on different locations in video sequence weakens its discriminative ability, especially when we
apply standard temporal aggregation such as 3D convolution
or LSTM. To address this issue, we propose Self-Separated
network (SSN) to seek out the same parts in different images.
As the name implies, SSN, if trained in an unsupervised strategy, guarantees the selected parts distinct. With a few samples
of labeled parts to guide SSN training, this semi-supervised
trained SSN seeks out the parts that are human-understandable
within a frame and stable across a video snippet. Given the
distinct and stable person parts, rather than performing agggregation on features, we then apply 3D convolution across
different frames for person re-identification. This SSN + 3D
pipeline, dubbed SSN3D, is proved to be efficient through
extensive experiments on both synthetic and real data