• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

3D Res-Inception Network Transfer Learning for Multiple Label Crowd Behavior Recognition

Vol. 13, No.3, March 31, 2019
10.3837/tiis.2019.03.019, Download Paper (Free):

Abstract

The problem towards crowd behavior recognition in a serious clustered scene is extremely challenged on account of variable scales with non-uniformity. This paper aims to propose a crowed behavior classification framework based on a transferring hybrid network blending 3D res-net with inception-v3. First, the 3D res-inception network is presented so as to learn the augmented visual feature of UCF 101. Then the target dataset is applied to fine-tune the network parameters in an attempt to classify the behavior of densely crowded scenes. Finally, a transferred entropy function is used to calculate the probability of multiple labels in accordance with these features. Experimental results show that the proposed method could greatly improve the accuracy of crowd behavior recognition and enhance the accuracy of multiple label classification.


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Cite this article

[IEEE Style]
Hao Nan, Min Li, Lvyuan Fan and Minglei Tong, "3D Res-Inception Network Transfer Learning for Multiple Label Crowd Behavior Recognition," KSII Transactions on Internet and Information Systems, vol. 13, no. 3, pp. 1450-1463, 2019. DOI: 10.3837/tiis.2019.03.019

[ACM Style]
Nan, H., Li, M., Fan, L., and Tong, M. 2019. 3D Res-Inception Network Transfer Learning for Multiple Label Crowd Behavior Recognition. KSII Transactions on Internet and Information Systems, 13, 3, (2019), 1450-1463. DOI: 10.3837/tiis.2019.03.019