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 region-based 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.
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Acknowledgement
The experimental work was partially supported by Shandong Provincial Key Laboratory of Automotive Electronics and Technology, Institute of Automation, Shandong Academy of Sciences.
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Gu, Q., Yang, J., Yan, W.Q., Li, Y., Klette, R. (2018). Local Fast R-CNN Flow for Object-Centric Event Recognition in Complex Traffic Scenes. In: Satoh, S. (eds) Image and Video Technology. PSIVT 2017. Lecture Notes in Computer Science(), vol 10799. Springer, Cham. https://doi.org/10.1007/978-3-319-92753-4_34
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DOI: https://doi.org/10.1007/978-3-319-92753-4_34
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