Object Recognition using Lucas-Kanade Technique and Support Vector Machine Based Classification in Video Surveillance Systems

Object recognition in video surveillance systems is the primary and most significant challenge task in the field of image processing. Video Surveillance systems provides us continuous monitoring of the objects for the enhancement of security and control. This paper presents novel approach recognizing the objects using Shi-Tomasi approach for detecting the corners of the object and then applies the Lucas-Kanade techniques to extract the features of the objects. The main objective of this paper is providing precise recognition of objects and estimation of their location from an unknown scene. Whenever the object is recognized from extracted frames of the input video the background subtraction will be applied. Then the classification of the objects into their respective categories can be achieved using support vector machine classifier by supervised learning. In case of multiple objects of different classes in a single frame, a vector containing the classes of all the detected in that frame is produced as output. The results of this work are drawn in the MATLAB tool by considering the input video dataset taken from various sources and extracting the frames from the input video for the detection then the efficiency of the proposed techniques will be measured.

Download Full-text

Image Normalization and Weighted Classification Using an Efficient Approach for SVM Classifiers

International Journal of Image and Graphics ◽

10.1142/s0219467820500357 ◽

2020 ◽

Vol 20 (04) ◽

pp. 2050035

Author(s):

Sumit Dhariwal ◽

Sellappan Palaniappan

Keyword(s):

Support Vector Machine ◽

Image Classification ◽

Error Rate ◽

Kernel Functions ◽

Support Vector ◽

Image Normalization ◽

Novel Approach ◽

Low Levels ◽

To Come

The content of massive image changing the brightest brightness is an impasse between most tests of sorted image realizations with low-resolution representation. I have done this research through image security, which will help curb crime in the coming days, and we propose a novel receipt for their strong and effective counterpart. Image classification using low levels of the image is a difficult method, so for this, I have adopted the method of automating the semantic image classification of this research and used it with different SVM classifiers, based on the normalized weighted feature support vector machine for semantic image classification. This is a novel approach given that weighted feature or normalized biased feature is applied and it is found that the normalized method is the best. It also uses normalized weighted features to compute kernel functions and train SVM. The trained SVM is then used to classify new images. During training and generalization, we displayed a decrease of identification error rate and there have been many benefits of using SVM with better performance in normalized image-cataloging systems. The importance of this technique and its role will be highlighted in the years to come.

Download Full-text

A NOVEL APPROACH FOR THE EXTRACTION AND CLASSIFICATION OF TUMOR IN MR IMAGES OF THE BRAIN VIA PRINCIPLE COMPONENT ANALYSIS AND KERNEL SUPPORT VECTOR MACHINE

International Journal of Advanced Research in Computer Science ◽

10.26483/ijarcs.v8i7.4585 ◽

2017 ◽

Vol 8 (7) ◽

pp. 1206-1211

Author(s):

Meenakshi Pareek ◽

Keyword(s):

Support Vector Machine ◽

Principle Component Analysis ◽

Component Analysis ◽

Support Vector ◽

Mr Images ◽

Principle Component ◽

Novel Approach ◽

Kernel Support Vector Machine ◽

The Brain

Download Full-text

NEURAL NETWORKS FOR COLLECTIVE TRANSLATIONAL INVARIANT OBJECT RECOGNITION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001492000084 ◽

1992 ◽

Vol 06 (01) ◽

pp. 143-156 ◽

Cited By ~ 1

Author(s):

LAI-WAN CHAN

Keyword(s):

Neural Networks ◽

Object Recognition ◽

Object Identification ◽

Training Procedure ◽

Multiple Objects ◽

Novel Approach ◽

Backpropagation Network ◽

Invariant Object Recognition ◽

Novel Method ◽

Recognition Of Objects

A novel method using neural networks for translational invariant object recognition is described in this paper. The objective is to enable the recognition of objects in any shifted position when the objects are presented to the network in only one standard location during the training procedure. With the presence of multiple or overlapped objects in the scene, translational invariant object recognition is a very difficult task. Noise corruption of the image creates another difficulty. In this paper, a novel approach is proposed to tackle this problem, using neural networks with the consideration of multiple objects and the presence of noise. This method utilizes the secondary responses activated by the backpropagation network. A confirmative network is used to obtain the object identification and location, based on these secondary responses. Experimental results were used to demonstrate the ability of this approach.

Download Full-text

Classification of Child Items in a Gold Tree using Support Vector Machine Classifier

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d8026.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 3208-3216

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Support Vector Machine Classifier ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Svm Classifier ◽

Main Application ◽

Novel Approach

Sorting of images has been a challenge in Machine Learning Algorithms over the years. Various algorithms have been proposed to sort an image but none of them are able to sort the image clearly. The drawback of the existing systems is that the sorted image is not clearly identified. So, to overcome this drawback we have proposed a novel approach to sort the children of a tree and match them with the existing designs. The images will be sorted on the basis of the class of the image. The images are taken from the image and manual binning of those images are done. Then the images are trained and tested. GLCM feature is extracted from the trained and tested images which are later on fed to the SVM classifier. The classification of image is then done with the help of SVM classifier. Around 7000 images are trained on SVM and used for classification. More than 300 different classes have been created in the database for comparison. Realtime images of child items are captured and fed to the SVM for classifying. The main application of this image is the use in distinguishing the designs in the ornaments. The various parts of the ornaments can be differentiated clearly. Thus, the proposed method is precise as compared to the existing methods.

Download Full-text