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

Nuvoola Inc.正在开发一个多因素认知分析引擎，该引擎依赖于多个信息源，并利用人工智能、云计算和业务规则将这些信息源映射为可操作的智能。该引擎的应用被认为是医疗保健、安全、物流、零售、金融、政府等领域的应用。认知分析引擎的主要组件是传感器融合模块，该模块将来自分布在空间和时间上的不同因素的信息源进行组合，例如，在一个实施例中，摄像头、麦克风、传感器等，提高对行动的信心。 ** 在视频监控中，人员重新识别任务是Nuvoola非常感兴趣的用例，其中必须通过分布式摄像机网络随着时间的推移识别个人。然而，由于相机视点、捕获条件（例如，姿态、照明、模糊）、背景杂波和遮挡。鉴于深度学习在许多具有挑战性的视觉识别问题上实现了最先进的准确性，Nuvoola试图评估深度卷积神经网络（CNN），以设计其多因素认知分析引擎的传感器融合模块的时空版本。** 在这个项目中，传感器融合模块结合使用视频摄像机获取的图像数据，并将研究和开发一些专门的时空识别算法。特别令人感兴趣的是适用于精确度量学习和识别的Siamese CNN，可能通过大规模视频数据集的域适应。因此，本项目还将评估允许域自适应的几种Siamese CNN架构的性能。该项目中开发和评估的技术对Nuvoola（作为认知分析引擎的用例）以及整个计算机视觉和机器学习社区都非常感兴趣。** ********** ****************

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.** ********** ****************