Object-Recognition with oblique observation directions Based on Biomimetic Pattern Recognition

Wang, Shoujue; Chen, Xu; Li, Weijun

doi:10.1109/icnnb.2005.1614915

Cited by 1 publication

(1 citation statement)

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“…These images will be processed by the system to detect and classify the intended moving objects. In order to test the classifier accuracy, our proposed method was compared to the notable approaches in this field used by [17] and [20] on the real traffic video datasets maintained by KOGS-IAKS, University of Karlsruhe. The proposed algorithm runs on a workstation with Intel ® i7-4700MQ 2.40 GHz CPU, and the software is written on MATLAB platform.…”

Section: Resultsmentioning

confidence: 99%

Moving Object Detection and Classification Using Neuro-Fuzzy Approach

Rashidan¹,

Mustafah²,

Shafie³

et al. 2016

IJMUE

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Public surveillance monitoring is rapidly finding its way into Intelligent Surveillance System. Street crime is increasing in recent years, which has demanded more reliable and intelligent public surveillance system. In this paper, the ability and the accuracy of an Adaptive Neuro-Fuzzy Inference System (ANFIS) was investigated for the classification of moving objects for street scene applications. The goal of this paper is to classify the moving objects prior to its communal attributes that emphasize on three major processes which are object detection, discriminative feature extraction, and classification of the target. The intended surveillance application would focus on street scene, therefore the target classes of interest are pedestrian, motorcyclist, and car. The adaptive network based on Neuro-fuzzy was independently developed for three output parameters, each of which constitute of three inputs and 27 Sugeno-rules. Extensive experimentation on significant features has been performed and the evaluation performance analysis has been quantitatively conducted on three street scene dataset, which differ in terms of background complexity. Experimental results over a public dataset and our own dataset demonstrate that the proposed technique achieves the performance of 93.1% correct classification for street scene with moving objects, with compared to the solely approaches of neural network or fuzzy.

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Section: Resultsmentioning

confidence: 99%