Real time object tracking to remove occlusion using OpenCV
Aniruddh Thakor, Anjali Askhedkar · 2014
Real time Object tracking is becoming a challenging ingredient in analysis of video imagery for efficient and robust object tracking. In this paper we presents a how to remove occlusion problem from real time video, removing occlusion from video is still a challenging part. Object tracking with sparse Prototypes, exploits both classic Principal Component Analysis (PCA) algorithms and sparse representation algorithms for learning appearance models. Here, regularization into the PCA reconstruction is introduced and this algorithm to represent an object by sparse prototypes that account precisely for data and noise is developed. In order to reduce tracking drift, a method that considers occlusion and motion blur into account rather than simply image observations for model update is given. The proposed tracking algorithm performs favorably against various methods that can be demonstrated by both qualitative and quantitative estimations on challenging image sequences. Index Terms— Object tracking, sparse prototype, Principle component analysis (PCA), L1 minimization, incremental visual tracking (IVT) and Compressive tracking —————————— ——————————