Performance Analysis of Alpha Beta Filter, Kalman Filter and Meanshift for Object Tracking in Video Sequences

Abstract

Abstract-ObjectTracking is becoming increasingly important in areas of computer vision, surveillance, image processing and artificial intelligence. The advent of high powered computers and the increasing need of video analysis has generated a great deal of interest in object tracking algorithms and its applications. This said it becomes even more important to evaluate these algorithms to quantify their performance. In this paper, we have implemented three algorithms namely Alpha Beta filter, Kalman filter and Meanshift to track an object in a video sequence and compared their tracking performance based on various parameters in normal and noisy conditions. The proposed parameters employed are error plots in position and velocity of the object, Root mean square error, object tracking error, tracking rate and time taken to track the object. The goal is to illustrate practically the performance of each algorithm under such conditions quantitatively and identify the algorithm that performs the best.

A Novel Kalman Filter Design and Analysis Method Considering Observability and Dominance Properties of Measurands Applied to Vehicle State Estimation

“…, setups 3 and 4 deliver worse observability than setups 1 and 2. This phenomenon is also reflected in the estimation accuracy shown in 6 (see Figure 8), where significant underestimation is present in both setups. In maneuver segments with isolated longitudinal excitation, namely about the vehicle velocity and additionally supports the estimation of ̂ in such moments.…”

“…However, neither an exact relationship between these values nor a calculation rule is presented. A similar relationship is presented in [6] using the example of a video tracking system. In [7], a method is shown where the filter is considered as a control system, thus allowing corresponding tuning criteria to be derived.…”

“…covariance matrices) Structure (e.g. states, measurements,..) Determination via an analytical dependency [4] Via relationship of parameterization and performance values [5] Tuning criteria considering the filter as a control system [6] Via comparison of performance and filter model parameters [7] Optimization via genetic algorithm [8],…”

Image Training, Corner and FAST Features based Algorithm for Face Tracking in Low Resolution Different Background Challenging Video Sequences

“…But, the algorithm is capable of tracking only single face in video sequences captured using unmoving camera and moving face(s) category. Ranganatha S et al [17] have developed another algorithm by integrating CAMSHIFT [14] and Kalman filter [18][19][20]. Kalman filter minimizes noise and updates current frame information to the next frame in video.…”

Kalman Filter for Moving Object Tracking: Performance Analysis and Filter Design

“…However, this method also requires an empirical selection of the dynamic models. A detailed analysis of the Kalman filter has been provided for various applications, including global navigation satellite systems [14] and video trackers [15]. However, only limited systems have yet been considered, and no definitive parameter-setting procedure for the Kalman tracking filter has been provided.…”