A constructing vehicle intrusion detection algorithm based on BOW presentation model

Yuan, Jie; Cheng, Weihua; Sun, Lingqing; Cheng, Yiqing

doi:10.1109/siprocess.2016.7888249

Cited by 2 publications

(1 citation statement)

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“…Y Cai proposed a monocular and binocular vision method based on Deep Convolutional Neural Networks to search vehicle for the whole area of image [1,2,18,27]. Jie Yuan, Thanida Tangkocharoen, Zhaojin Zhang, Q Jiang and Hulin Kuang et al used multi-layer classifiers, deep learning classifiers or neural networks to recognize the vehicles [6,8,20,23,24]. These methods used sliding and multiscale windows to scan the whole image and find the vehicle targets.…”

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confidence: 99%

Research on regional clustering and two-stage SVM method for container truck recognition

Mi¹,

Wang²,

Mi³

et al. 2019

DCDS-S

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With large-scale, integrated, intelligence for ports, many ports begin to use intelligent detection systems to make their operations more efficient. The container truck recognition and positioning system is also beginning to apply into container quayside to assist the joint operations between quay cranes and container trucks. However, the traditional vehicle detection by using motion region detection cannot recognize the type of moving object, and the traditional pattern recognition method cannot meet the requirements in real-time operation. In order to solve these problems, an algorithm fused by regional clustering and two-stage SVM classifier is proposed in this paper. The method consists of two phases, which are independently executed in two camera systems on quay cranes. In the first stage, a fast motion regional clustering algorithm is used to detect moving image patches as the truck candidate sub-windows. In the second stage, the container trucks will be recognized in these sub-windows by an optimized two-stage SVM classifier. Compared with existing traditional algorithm, experimental results in container terminal show that the fusion algorithm with regional clustering and two-stage SVM has higher efficiency and better truck recognition performance.

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confidence: 99%