A learning-based approach for leaf detection in traffic surveillance video

被引:2
作者
Chen, Li [1 ,2 ]
Peng, Xiaoping [1 ,2 ]
Tian, Jing [1 ,2 ]
Liu, Jiaxiang [1 ,2 ]
机构
[1] Wuhan Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430081, Hubei, Peoples R China
[2] Wuhan Univ Sci & Technol, Hubei Prov Key Lab Intelligent Informat Proc & Re, Wuhan 430081, Hubei, Peoples R China
基金
中国国家自然科学基金;
关键词
Leaf detection; Video surveillance; Probabilistic model; Feature extraction; IMAGES; LEAVES;
D O I
10.1007/s11045-017-0540-6
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Traffic surveillance video is recorded in uncontrolled outdoor scenarios. If the camera view gets obstructed by the leaves, the video will fail to be used in vehicle tracking and recognition. It is required that the traffic video surveillance systems run self-checking in order to evaluate if the camera view is blocked by leaves or not. In view of this, a two-step learning framework is proposed in this paper to automatically determine whether the video is leaf degraded or leaf free. First, the proposed framework exploits the convolutional neural network to learn the discriminative features of leaf particles. Then the trained model is used to detect candidate leaf patches in the image. Second, a probabilistic approach is used to pool decisions of each candidate leaf patch to generate final leaf detection result in the video. Experimental results are provided to demonstrate that the proposed approach can effectively detect leaves in real-world traffic surveillance video.
引用
收藏
页码:1895 / 1904
页数:10
相关论文
共 24 条
[1]   Detecting External Disturbances on the Camera Lens in Wireless Multimedia Sensor Networks [J].
Alippi, Cesare ;
Boracchi, Giacomo ;
Camplani, Romolo ;
Roveri, Manuel .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2010, 59 (11) :2982-2990
[2]  
Babatunde O., 2015, Journal of Agricultural Informatics, V6, P61
[3]   Identifying Central Features of Cotton Leaves in Digital Images with Difficult Backgrounds [J].
Barbedo, J. G. A. .
IEEE LATIN AMERICA TRANSACTIONS, 2015, 13 (09) :3072-3079
[4]  
Bottou L, 2012, Neural Networks: Tricks of the Trade, P421, DOI 10.1007/978-3-642-35289-8_25
[5]   Spatio-temporal analysis for obstacle detection in agricultural videos [J].
Campos, Yerania ;
Sossa, Humberto ;
Pajares, Gonzalo .
APPLIED SOFT COMPUTING, 2016, 45 :86-97
[6]  
Cerutti G, 2013, IEEE IMAGE PROC, P1471, DOI 10.1109/ICIP.2013.6738302
[7]   LIBSVM: A Library for Support Vector Machines [J].
Chang, Chih-Chung ;
Lin, Chih-Jen .
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2011, 2 (03)
[8]   Dust particle detection in traffic surveillance video using motion singularity analysis [J].
Chen, Li ;
Zhu, Dawei ;
Tian, Jing ;
Liu, Jiaxiang .
DIGITAL SIGNAL PROCESSING, 2016, 58 :127-133
[9]  
Daw-Tung Lin, 2012, 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), P383, DOI 10.1109/IIH-MSP.2012.99
[10]  
Dudek R, 2009, LECT NOTES COMPUT SC, V5857, P800, DOI 10.1007/978-3-642-05036-7_76