CNN-based video analytics

The content of this thesis illustrates the six months work done during my internship at TKH Security Solutions - Siqura B.V. in Gouda, Netherlands. The aim of this thesis is to investigate on convolutional neural networks possible usage, from two different point of view: first we propose a novel algorithm for person re-identification, second we propose a deployment chain, for bringing research concepts to product ready solutions. In existing works, the person re-identification task is assumed to be independent of the person detection task. In this thesis instead, we consider the two tasks as linked. In fact, features produced by an object detection convolutional neural network (CNN) contain useful information, which is not being used by current re-identification methods. We propose several solutions for learning a metric on CNN features to distinguish between different identities. Then the best of these solutions is compared with state of the art alternatives on the popular Market-1501 dataset. Results show that our method outperforms them in computational efficiency, with only a reasonable loss in accuracy. For this reason, we believe that the proposed method can be more appropriate than current state of the art methods in situations where the computational efficiency is critical, such as embedded applications. The deployment chain we propose in this thesis has two main goals: it must be flexible for introducing new advancement in networks architecture, and it must be able to deploy neural networks both on server and embedded platforms. We tested several frameworks on several platforms and we ended up with a deployment chain that relies on the open source format ONNX.