Multiple Moving Object Recognitions in video based on Log Gabor-PCA Approach

TL;DR

This paper presents a robust method for recognizing multiple moving objects in videos using Log Gabor-PCA and angle-based similarity measures, effective in complex environments like low resolution and foggy videos.

Contribution

Introduces a novel Log Gabor-PCA based approach combined with angle similarity measures for improved moving object recognition in challenging video conditions.

Findings

Achieves high recognition accuracy in indoor and outdoor videos.

Effective in low resolution, foggy, and dim video sequences.

Demonstrates robustness across standard and custom datasets.

Abstract

Object recognition in the video sequence or images is one of the sub-field of computer vision. Moving object recognition from a video sequence is an appealing topic with applications in various areas such as airport safety, intrusion surveillance, video monitoring, intelligent highway, etc. Moving object recognition is the most challenging task in intelligent video surveillance system. In this regard, many techniques have been proposed based on different methods. Despite of its importance, moving object recognition in complex environments is still far from being completely solved for low resolution videos, foggy videos, and also dim video sequences. All in all, these make it necessary to develop exceedingly robust techniques. This paper introduces multiple moving object recognition in the video sequence based on LoG Gabor-PCA approach and Angle based distance Similarity measures techniques used to recognize the object as a human, vehicle etc. Number of experiments are conducted for indoor and outdoor video sequences of standard datasets and also our own collection of video sequences comprising of partial night vision video sequences. Experimental results show that our proposed approach achieves an excellent recognition rate. Results obtained are satisfactory and competent.