A Comparison of Siamese Convolutional Neural Networks for Person Re-Identification in Video Surveillance**

点击翻译按钮获取中文摘要

Nuvoola Inc. is developing a multi-factor cognitive analytics engine that relies on multiple sources of information, and leverages artificial intelligence, cloud computing, and business rules to map these sources into actionable intelligence. Applications for this engine are considered for applications in healthcare, security, logistics, retail, finance, government, etc. A main component of the cognitive analytics engine is the sensor fusion module that combines sources of information from the different factors that are distributed over space and time., e.g., cameras, microphones, sensors, etc., to improve confidence on actions. ** In video surveillance, the person re-identification task is a use case of great interest to Nuvoola, where individuals must be recognized over time, across a distributed network of video cameras. However, the performance of techniques for person re-identification is typically poor in practice due to variations in camera viewpoints, capture conditions (e.g., pose, illumination, blur), background clutter and occlusion. Given the state-of-the-art accuracy achieved with deep learning on many challenging visual recognition problems, Nuvoola seeks to evaluate deep convolutional neural networks (CNNs) to design a spatial-temporal version of the sensor fusion module of their multi-factor cognitive analytics engine. ** For this project, the sensor fusion module combines image data acquired using video cameras, and some specialized algorithms will be investigated and developed for spatial-temporal recognition. Of a particular interest are Siamese CNNs that are suitable for accurate metric learning and recognition, potentially through domain adaptation on large-scale video datasets. Accordingly, the performance of several Siamese CNN architectures that allow for domain adaptation will also be evaluated in this project. The techniques developed and evaluated in this project are of great interest to Nuvoola (as a use case of the cognitive analytics engine), and to the computer vision and machine learning communities in general.** ********** ****************