Common tracking systems are usually affected by environmental and technical interferences such as non-uniform illumination, sensors' noise, geometrical scene distortion, etc. Among these issues, the former is particularly interesting because it destroys important spatial characteristics of objects in observed scenes. We propose a two-step tracking algorithm: first, preprocessing locally normalizes the illumination difference between the target object and observed frames; second, the normalized object is tracked by means of a designed template structure based on histograms of oriented gradients and kinematic prediction model. The algorithm performance is tested in terms of recognition and localization errors in real scenarios. In order to achieve high rate of the processing, we use GPU parallel processing technologies. Finally, our algorithm is compared with other state-of-the-art trackers.
In recent years, human-computer interaction (HCI) has received a lot of interest in industry and science because it provides new ways to interact with modern devices through voice, body, and facial/hand gestures. The application range of the HCI is from easy control of home appliances to entertainment. Hand gesture recognition is a particularly interesting problem because the shape and movement of hands usually are complex and flexible to be able to codify many different signs. In this work we propose a three step algorithm: first, detection of hands in the current frame is carried out; second, hand tracking across the video sequence is performed; finally, robust recognition of gestures across subsequent frames is made. Recognition rate highly depends on non-uniform illumination of the scene and occlusion of hands. In order to overcome these issues we use two Microsoft Kinect devices utilizing combined information from RGB and infrared sensors. The algorithm performance is tested in terms of recognition rate and processing time.
In recent years tracking applications with development of new technologies like smart TVs, Kinect, Google Glass and Oculus Rift become very important. When tracking uses a matching algorithm, a good prediction algorithm is required to reduce the search area for each object to be tracked as well as processing time. In this work, we analyze the performance of different tracking algorithms based on prediction and matching for a real-time tracking multiple objects. The used matching algorithm utilizes histograms of oriented gradients. It carries out matching in circular windows, and possesses rotation invariance and tolerance to viewpoint and scale changes. The proposed algorithm is implemented in a personal computer with GPU, and its performance is analyzed in terms of processing time in real scenarios. Such implementation takes advantage of current technologies and helps to process video sequences in real-time for tracking several objects at the same time.
A common tracking algorithm solves several problems such as detection and localization of an object of interest in the
scene, invariance to different movement directions of the object, occlusion of the target, when the target exits or reenters
to the scene and so on. Nowadays the frame rates are various from 30 FPS (frames per second) to 300 FPS in special
devices. Tracking algorithms can be classified as follows: point trackers, kernel trackers, and silhouette trackers. In this
work, we propose a kernel tracking algorithm based on a local gradient histogram matching algorithm and prediction of
the target position in video sequence frames.
Image matching is an important task in image processing. Basically two different problems are distinguished: detection of a reference image in a scene and estimation of its exact position. Recently many matching algorithms have been proposed. In this work, we propose a hybrid matching algorithm based on recursive calculation of local gradient histograms and pyramidal representation of matched images. The proposed algorithm is fast and invariant to affine transformations such as rotation, translation, and scaling. Computer simulation results obtained with the suggested algorithm are presented and compared with those of common matching techniques.