Moving Object Detection and Classification Using Neuro-Fuzzy Approach

Abstract

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.

EspiNet V2: a region based deep learning model for detecting motorcycles in urban scenarios

“…Motorcycle detection [20,21] starts with this technique and uses segmentation to detect and separate motorcycles in the analysis. In some works, a similar approach is used, even to detect motorcycle riders without a helmet [24][25][26][27][28][29].…”

“…The most used algorithm for background subtraction is Gaussian Mixture Models (GMM) [22], used in [23,24]. For dealing with object shadows and for continuous update of parameters, Self Adaptive GMM [25] is used in [26] or adaptive background modelling used in [14] and [15].…”

“…Motorcycle detection in [24] uses spatial features in conjunction with motion features obtained from optical flow, this type of features is useful for obstacle detection in a Lane Change Assistant (LCA) system [10].…”

“…Nevertheless, NN are used for helmet detection in [16,31]. There is also Fuzzy neural network (FNN) [24], but without a significant number of motorcycles to detect in their dataset. Finally, K-Nearest Neighbor (KNN) is also used for Helmet detection [23]; nevertheless, this model relies on the background subtraction accuracy for motorcycle individualisation, which may fail in occluded scenarios.…”

Motorcycle detection and classification in urban Scenarios using a model based on Faster R-CNN

“…Based on object proposal algorithms, two stage CNN models integrate region proposal and classification in a single architecture, such as Fast R-CNN [29] and faster R-CNN [30] based models for vehicle detection and classification [31][32] [33]. Motivated by safety measures, helmet detection in motorcycle riders has inspired research using geometrical features [34], hand crafted features (HOG, SIFT, LBP, CHT [35] [36] [12]), neuro-fuzzy detectors [37] and neural networks [38]. Nevertheless, there are few reports exploring CNNs for motorcycle classification, e.g.…”