Video Target Tracking Research Articles

Combining multiple scoring systems for target tracking using rank–score characteristics

Video target tracking is the process of estimating the current state, and predicting the future state of a target from a sequence of video sensor measurements. Multitarget video tracking is complicated by the fact that targets can occlude one another, affecting video feature measurements in a highly non-linear and difficult to model fashion. In this paper, we apply a multisensory fusion approach to the problem of multitarget video tracking with occlusion. The approach is based on a data-driven method (CFA) to selecting the features and fusion operations that improve a performance criterion. Each sensory cue is treated as a scoring system. Scoring behavior is characterized by a rank–score function. A diversity measure, based on the variation in rank–score functions, is used to dynamically select the scoring systems and fusion operations that produce the best tracking performance. The relationship between the diversity measure and the tracking accuracy of two fusion operations, a linear score combination and an average rank combination, is evaluated on a set of 12 video sequences. These results demonstrate that using the rank–score characteristic as a diversity measure is an effective method to dynamically select scoring systems and fusion operations that improve the performance of multitarget video tracking with occlusions.

Video target tracking by using competitive neural networks

A target tracking algorithm able to identify the position and to pursuit moving targets in video digital sequences is proposed in this paper. The proposed approach aims to track moving targets inside the vision field of a digital camera. The position and trajectory of the target are identified by using a neural network presenting competitive learning technique. The winning neuron is trained to approximate to the target and, then, pursuit it. A digital camera provides a sequence of images and the algorithm process those frames in real time tracking the moving target. The algorithm is performed both with black and white and multi-colored images to simulate real world situations. Results show the effectiveness of the proposed algorithm, since the neurons tracked the moving targets even if there is no pre-processing image analysis. Single and multiple moving targets are followed in real time.

Multiscale and local search methods for real time region tracking with particle filters: local search driven by adaptive scale estimation on GPUs

Tracking systems are important in computervision, with applications in surveillance, human computer interaction, etc. Consumer graphics processing units (GPUs) have experienced an extraordinary evolution in both computing performance and programmability, leading to greater use of the GPU for non-rendering applications. In this work we propose a real-time object tracking algorithm, based on the hybridization of particle filtering (PF) and a multi-scale local search (MSLS) algorithm, presented for both CPU and GPU architectures. The developed system provides successful results in precise tracking of single and multiple targets in monocular video, operating in real-time at 70 frames per second for 640 × 480 video resolutions on the GPU, up to 1,100% faster than the CPU version of the algorithm.

Feature Tracking in Video and Sonar Subsea Sequences with Applications

This paper deals with automatic target tracking in video and sonar subsea sequences, an essential capability for automating tasks currently performed by remotely operated vehicles under pilot control. We describe two trackers, one for video sequences, the other for sector scan sonar sequences. No assumptions are made about the images, scene, or motion observed. To illustrate applications, we report results of our systems for 3-D structure reconstruction and panoramic mosaic building from video sequences and describe in some detail our path planning and obstacle avoidance system using sonar sequences.