In this proposed work, the moving object is localized using curvelet transform, soft thresholding and frame differencing. The feature extraction techniques are applied on to the localized object and the texture, color and shape information of objects are considered. To extract the shape information, Speeded Up Robust Features (SURF) is used. To extract the texture features, the Enhanced Local Vector Pattern (ELVP) and to extract color features, Histogram of Gradient (HOG) are used and then reduced feature set obtained using genetic algorithm are fused to form a single feature vector and given into the Extreme Learning Machine (ELM) to classify the objects. The performance of the proposed work is compared with Naive Bayes, Support Vector Machine, Feed Forward Neural Network and Probabilistic Neural Network and inferred that the proposed method performs better.
Object tracking is a well-studied problem in computer vision and has many practical applications. The problem and its difficulty depend upon several factors such as the knowledge about the target object, its quantity and type of parameters being tracked. Although there has been some success with building trackers for specific object classes such as human, face, mice etc. Tracking generic objects has remained challenging issue because an object can drastically change its appearance when deforming, rotating out of plane or when the illumination of the scene changes. Especially in the videos which are not clear in its original form i.e. the video which requires enhancement and its quality has to be improved. Here in the proposed work different algorithms are carried out to detect the generic objects such as the non-rigid objects and the deformable objects which are in occlusion, (i.e. in a cluttered environment) not only in the images which contain some sort of objects but also from the images which contains numerous objects which are unseen so as to improve the tracking efficiency. By doing this, the objects which are non-rigid and changing in nature can also be predicted in a perfect manner so that the computational complexity can be reduced, reliability, accuracy, and efficiency can be improved.
The feature extraction technique is applied on least enclosing rectangle (LER) of the segmented object to increase the processing speed. The main intuition of this salp swarm algorithm relays on reducing the computational load of the proposed classifier by removing the repetitive and unrelated features from the feature vector. Also, increased training samples of similarly shaped classes when applied on the classifier can generate the misclassification results. Thus, a new layered kernel-based support vector machine (k-SVM) classifier is developed by means of integrating the k-neural network classifier and layered SVM classifier. Because of the high dimensional features, a difficulty occurs in the application of a single classifier. In order to ease the computational load, this multi classifier is integrated with a shadow elimination technique to classify the object categories of intelligent transportations system such as motorcycles, bicycles, cars, and pedestrians.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.