Integrated Hidden Markov Model and Kalman Filter for Online Object Tracking

Visual prior from generic real-world images study to represent that objects in a scene. The existing work presented online tracking algorithm to transfers visual prior learned offline for online object tracking. To learn complete dictionary to represent visual prior with collection of real world images. Prior knowledge of objects is generic and training image set does not contain any observation of target object. Transfer learned visual prior to construct object representation using Sparse coding and Multiscale max pooling. Linear classifier is learned online to distinguish target from background and also to identify target and background appearance variations over time. Tracking is carried out within Bayesian inference framework and learned classifier is used to construct observation model. Particle filter is used to estimate the tracking result sequentially however, unable to work efficiently in noisy scenes. Time sift variance were not appropriated to track target object with observer value to prior information of object structure. Proposal HMM based kalman filter to improve online target tracking in noisy sequential image frames. The covariance vector is measured to identify noisy scenes. Discrete time steps are evaluated for identifying target object with background separation. Experiment conducted on challenging sequences of scene. To evaluate the performance of object tracking algorithm in terms of tracking success rate, Centre location error, Number of scenes, Learning object sizes, and Latency for tracking.