Moving object detection and classification using neuro-fuzzy approach

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

Moving object recognition by a shape-based neural fuzzy network

Moving object recognition by a shape-based neural fuzzy network is proposed in this paper. The moving objects considered in this paper include pedestrians, vehicles, motorcycle, and dogs. Given the shape of the moving object, its contour is calculated by contour following. The distance between the contour center and each contour point is calculated and smoothed. Parts of the feature vector are obtained from discrete Fourier transform coefficients of the smoothed distances. The length-to-width ratio of the object's shape, which is derived from vertical and horizontal projection of the shape of the object, is also used as a feature. Based on the feature vector, the self-constructing neural fuzzy inference network (SONFIN) is used for recognition. To verify the performance of the proposed approach, two experiments were performed. In the first experiment, the shape of an object was extracted manually. In the second experiment, the shape of an object was extracted automatically from a series of image processes, including gray-based and edge-based image subtractions and morphological operations. The experiments show that the proposed approach can recognize moving objects with high accuracy. SONFIN performance is also shown to be better than back-propagation neural network and radial basis function network performance. (c) 2007 Elsevier B.V. All rights reserved