A Hybrid Approach for Real-Time Object Detection and Tracking to Cover Background Turbulence Problem

Abstract

Objectives: A number of techniques available in literature do not discuss the problem of incorrect object detection in the scenes having unstable or moving background. A novel hybrid technique is proposed in the paper to cover this problem. Methods/Statistical Analysis: The new approach proposed is the hybrid of two well known techniques for object detection. One is frame-differencing approach and other is skin colour modelling. This newer technique exploits the fact that the demerit of one technique works as merit for other and hence hybrid technique resolves the problem of moving background or turbulence in background. A real time video having turbulence in background is used for testing the efficiency of the approach. Findings: With accuracy of 0.97, the proposed approach outperforms the individual approaches i.e. frame-differencing and skin colour modelling. A very low value of False Positive Rate (FPR) for proposed approach compared to other approaches confirms the least incorrect detections. High value of True Positive Rate (TPR) conveys that fall out of correct object is least in the video by proposed approach. Results show that the proposed approach better applicable for background with turbulence. Application/Improvements: Automatic object detection and tracking in applications as surveillance are tractable with the proposed hybrid approach.