Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154607
Title: Crowd counting for intelligent video surveillance
Authors: Chen, Pengyu
Keywords: Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Chen, P. (2021). Crowd counting for intelligent video surveillance. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/154607
Abstract: Surveillance plays an important role in maintaining public safety. Especially under the situation of COVID-19 recently, the flow of people needs to be monitored and strictly controlled at any time. However, this work usually costs plenty of time for humans to observe. Meanwhile, it is difficult to make an accurate estimation for crowds, especially in complex scenes. Fortunately, machine vision is an advanced technology that can help us complete this time-consuming task. With the rise of convolutional neural networks and deep learning, visual detectors can distinguish more types of objects, and they also have a wider range of applications. Meanwhile, the performance of these detectors has gradually improved, making it possible to use surveillance cameras to complete crowd detection tasks simultaneously. The video can be processed frame-by-frame as an image, and then the detector can automatically output prediction data, such as the total number of people, their faces’ locations and sizes, etc. In this dissertation, several object detection methods and the basic principles of the convolutional neural network are briefly introduced as fundamental knowledge. Besides, a simple and effective network with some modifications is discussed as the baseline of our method. Meanwhile, a self-training approach that enables the network to be trained using only point-level annotations is also introduced. Our method proposes to combine this training approach with the baseline to benefit from their powerful error correction and crowd analysis capabilities. Experimental results on the NWPU dataset show that our method is effective in crowd counting, crowd localization, and size prediction tasks.
URI: https://hdl.handle.net/10356/154607
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
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