Appearance-Based Tracking and Face Identification in Video Sequences.

We present a technique for face recognition in videos. We are able to recognise a face in a video sequence, given a single gallery image. By assuming that the face is in an approximately frontal position, we jointly model changes in facial appearance caused by identity and illumination. The identity of a face is described by a vector of appearance parameters. We use an angular distance to measure the similarity of faces and a probabilistic procedure to accumulate evidence for recognition along the sequence. We achieve 93.8% recognition success in a set of 65 sequences of 6 subjects from the La Cascia and Sclaroff database

An Efficient Hardware Implementation of Target Recognition Algorithms and Investigation of Secure Wireless Communication for a Modified Manet

This paper presents a scheme for effective wireless security of a open broadcast mobile ad-hoc network, MANET, network without significant loss of bandwidth and data integrity through a double tiered encryption scheme, and the feasibility of reducing the target tracking algorithm in [1] into a compact and efficient hardware package. Due to the open nature of MANET, modifications are necessary to secure wireless data in a potential hostile environment. Furthermore, due to power and processing limitations of small unmanned aerial vehicles (UAVs) and the processing intensive calculations of image processing, a sample hardware implementation of key functions of the target tracking algorithm is described. Using hardware simulation and modeling to implement key elements, results are compared against identical function blocks in a software environment. The results of this research allow for further work in open broadcast MANET security and target tracking hardware implementation to be confidently pursued; it also suggests the tools, methodology, and overall architecture for a larger project

Realtime tracking and grasping of a moving object from range video

In this paper we present an automated system that is able to track and grasp a moving object within the workspace of a manipulator using range images acquired with a Microsoft Kinect sensor. Realtime tracking is achieved by a geometric particle filter on the affine group. Based on the tracked output, the pose of a 7-DoF WAM robotic arm is continuously updated using dynamic motor primitives until a distance measure between the tracked object and the gripper mounted on the arm is below a threshold. Then, it closes its three fingers and grasps the object. The tracker works in real-time and is robust to noise and partial occlusions. Using only the depth data makes our tracker independent of texture which is one of the key design goals in our approach. An experimental evaluation is provided along with a comparison of the proposed tracker with state-of-the-art approaches, including the OpenNI-tracker. The developed system is integrated with ROS and made available as part of IRI's ROS stack.Peer ReviewedPostprint (author’s final draft

Tracking and Recognition: A Unified Approach on Tracking and Recognition

This paper proposes a unified approach on tracking and recognition .Object tracking is done at low level and recognition is done at high level. Traditional tracking methods give importance to low level image correspondences between frames. High level image correspondences are used for reliable tracking. Online and Offline models are used for both tracking and recognition which is done simultaneously. Thus high level offline model is combined with low level online model to increase the tracking performance. Onine model used for tracking is given to the video based recognition and at same time offline model plays important role to recognize the category of the object. This method is useful to handle difficult scenarios like abrupt change, background clutter, pose variations, occlusion and morphable objects. This is based on study of different IEEE papers

Tracking people in crowds by a part matching approach

The major difficulty in human tracking is the problem raised by challenging occlusions where the target person is repeatedly and extensively occluded by either the background or another moving object. These types of occlusions may cause significant changes in the person's shape, appearance or motion, thus making the data association problem extremely difficult to solve. Unlike most of the existing methods for human tracking that handle occlusions by data association of the complete human body, in this paper we propose a method that tracks people under challenging spatial occlusions based on body part tracking. The human model we propose consists of five body parts with six degrees of freedom and each part is represented by a rich set of features. The tracking is solved using a layered data association approach, direct comparison between features (feature layer) and subsequently matching between parts of the same bodies (part layer) lead to a final decision for the global match (global layer). Experimental results have confirmed the effectiveness of the proposed method. © 2008 IEEE

Suivi des mouvements faciaux et de la pose 2D d'un visage

Nous considérons dans cet article le problème du suivi de la pose et des mouvements faciaux d'un visage faisant face à une caméra. Pour cela, nous proposons une approche stochastique reposant sur le filtrage particulaire où la distribution des observations est dérivée soit d'un modèle d'apparence actif, soit d'un modèle d'apparence calculé en ligne. L'évolution du système d'état est décrite par une dynamique guidées par une recherche déterministe. Le nombre de particules est ajusté aux besoins effectifs du suivi à chaque pas temporel; cet ajustement permet l'optimisation du temps de calcul du filtre. La prise en compte d'une mesure robuste permet d'augmenter la robustesse du suivi lorsque le visage est partiellement occulté. L'article se termine par la présentation de résultats expérimentaux validant l'intérêt des méthodes de suivi reposant sur les deux modèles d'observation proposés