Compression of Convolutional Neural Networks for Efficient Real-Time Person Re-Identification Applications

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SPORTLOGiQ Inc. develops advanced sports analytics software to track the location and actions of playersappearing in sports game videos. This project focuses on the task of person re-identification in sports videos,where the same players should be recognized across multiple distributed cameras viewpoint, or across timewithin a single camera. In this context, the performance of person re-identification algorithms can decline dueto variations in player capture conditions, background clutter, and occlusions. Computational complexity isanother important consideration for real-time applications.This project aims to provide SPORTLOGiQ with the expertise needed to design accurate yet efficient systemsfor real-time person re-identification. Given the state-of-the-art accuracy achieved with deep learningarchitectures on many challenging visual recognition problems, SPORTLOGiQ seeks to develop and evaluatedeep convolutional neural networks (CNNs) for accurate person re-identification in sports videos. However,since these CNNs represent complex solutions for real-time applications, this project seeks to developspecialized techniques for compression of CNNs, to reduce their time and memory complexity. In this project,the performance of several techniques proposed in the literature to increase the computational efficiency ofCNNs will be evaluated and compared for real-time person re-identification on sports game videos. Theseinclude advanced techniques for search reduction, features selection, and parameter pruning. In particular, thisproject will focus mostly on promising new filter-level pruning techniques that can simultaneously accelerateand compress a CNN based on statistical information extracted from its layers. The techniques evaluated in thisproject are of great interest to SPORTLOGiQ and to the computer vision and machine learning communities ingeneral. The aim of this is research project is to solve one of the fundamental computer vision problems in thecontext of sports video analytics.