In today’s modern world of computer vision there are many techniques for object tracking. But still there is great capacity available for further research. A robust technique for object tracking is proposed in this paper. In this work a fusion of global motion estimation and Kalman filter-based tracking algorithm is implemented which detects and tracks all the moving objects in the video. The algorithm detects corners in a frame and tracks the moving ones in the subsequent frames of the input video. The movement of a moving object is traced by persisting the motion trajectory of corner points on that object. Video stabilization is also implemented so that the moving or shaky video can be processed and amended so that Kalman filter can be implemented. The proposed methodology achieved a precision of 94.73 percent which is quite good in comparison to other published techniques.

https://sujo2.usindh.edu.pk/index.php/USJICT/article/download/96/84

Robust Technique for Object Tracking by interference of Global Motion Estimation and Kalman Filter

Camera sensor networks (CSN) have been widely used in different applications such as large building monitoring, social security, and target tracking. With advances in visual and actuator sensor technology in the last few years, deploying mobile cameras in CSN has become a possible and efficient solution for many CSN applications. However, mobile camera sensor networks still face several issues, such as limited sensing range, the optimal deployment of camera sensors, and the energy consumption of the camera sensors. Therefore, mobile cameras should cooperate in order to improve the overall performance in terms of enhancing the tracking quality, reducing the moving distance, and reducing the energy consumed. In this paper, we propose a movement prediction algorithm to trace the moving object based on a cooperative relay tracking mechanism. In the proposed approach, the future path of the target is predicted using a pattern recognition algorithm by applying data mining to the past movement records of the target. The efficiency of the proposed algorithms is validated and compared with another related algorithm. Simulation results have shown that the proposed algorithm guarantees the continuous tracking of the object, and its performance outperforms the other algorithms in terms of reducing the total moving distance of cameras and reducing energy consumption levels. For example, in terms of the total moving distance of the cameras, the proposed approach reduces the distance by 4.6% to 15.2% compared with the other protocols that do not use prediction.

/pdf/energy-efficient-relay-tracking-and-predicting-movement-3i0psd7u.pdf

Energy-Efficient Relay Tracking and Predicting Movement Patterns with Multiple Mobile Camera Sensors

Object tracking systems using camera have become an essential requirement in today’s society. In-expensive and high-quality video cameras, availability and demand for analysis of automated video have produced a lot of interest for numerous fields. Almost all conventional algorithms are developed based on background subtraction, frame difference, and static background. They fail to track in environments such as variation in illumination, cluttered background, and occlusions. The image segmentation based object tracking algorithms fail to track in real-time. Feature extraction of an image is an indispensable first step in object tracking applications. In this paper, a novel real-time object tracking based on position and feature vectors is developed. The proposed algorithm involves two phases. The first phase is extraction of features for region of interest object in first frame and nine position features of second frame in video. The second phase is similarity estimation of extracted features of two frames using Euclidean distance. The nearest match is considered by minimum distance between first frame feature vectors and nine different feature vectors of second frame. The proposed algorithm is compared with other existing algorithms using different feature extraction techniques for object tracking in video. The proposed method is simulated and evaluated by statistical, discrete wavelet transform, Radon transform, scale-invariant feature transform and features from accelerated segment test. The performance evaluation shows that the proposed algorithm can be applied for any feature extraction technique and object tracking in video depends on tracking accuracy.

V. Purandhar Reddy

Papers

Object Tracking Based on Position Vectors and Pattern Matching