Local Fast R-CNN Flow for Object-centric Event Recognition in Complex Traffic Scenes
Date
Authors
Gu, Qin
Yang, Jianyu
Yan, Wei Qi
Li, Yanqiang
Klette, Reinhard
Supervisor
Item type
Conference Contribution
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Abstract
This paper presents a solution for an integrated object-centric event recognition problem for intelligent traffic supervision. We propose a novel event-recognition framework using deep local flow in a fast regionbased convolutional neural network (R-CNN). First, we use a fine-tuned fast R-CNN to accurately extract multi-scale targets in the open environment. Each detected object corresponds to an event candidate. Second, a deep belief propagation method is proposed for the calculation of local fast R-CNN flow (LFRCF) between local convolutional feature matrices of two non-adjacent frames in a sequence. Third, by using the LFRCF features, we can easily identify the moving pattern of each extracted object and formulate a conclusive description of each event candidate. The contribution of this paper is to propose an optimized framework for accurate event recognition. We verify the accuracy of multi-scale object detection and behavior recognition in extensive experiments on real complex road-intersection surveillance videos.Description
Keywords
Deep learning, Event recognition, Convolutional neural network, Belief propagation
Source
In: Satoh S. (eds) Image and Video Technology. PSIVT 2017. Lecture Notes in Computer Science, vol 10799. Springer, Cham
Publisher's version
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