Face Tracking Using Adaptive Appearance Models and Convolutional Neural Network

Boguslaw Rymut and Bogdan Kwolek: Face Tracking Using Adaptive Appearance Models and Convolutional Neural Network; Int. Conf. on Hybrid Artificial Intelligence Systems, 2011, Hybrid Artificial Intelligent Systems, Lecture Notes in Computer Science, Vol. 6678, Springer Berlin Heidelberg, 2011, pp. 271-279.

Abstract

One inherent problem of online learning based trackers is drift consisting in a gradual accommodation of the tracker to non-targets. This paper proposes an algorithm that does not suffer from the template drift inherent in a naive implementation of the online appearance models. The tracking is done via particle swarm optimization algorithm built on adaptive appearance models. The convolutional neural network based face detections are employed to support the re-diversification of the swarm in the course of the tracking. Such candidate solutions vote simultaneously towards true location of the face through correcting the fitness function. In particular, the hybrid algorithm has better recovery capability in case of tracking failure.

Face Tracking with Guaranteed Framerates on Mobile Phones

Bogusław Rymut and Bogdan Kwolek: Face Tracking with Guaranteed Framerates on Mobile Phones; Image Processing and Communications Challenges 3, Advances in Intelligent and Soft Computing, Vol. 102, Springer Berlin Heidelberg, 2011, pp. 165-172.

Abstract

This paper addresses the problem of face tracking with guaranteed framerates at mobile devices. The frame rate of the computationally inexpensive algorithm is not affected by the image content. An ellipse with fixed orientation is used to model the head. The position and the size of the ellipse are determined with respect to intensity gradient near the edge of the ellipse and skin color probability in the ellipse’s interior. The tracking is achieved using particle swarm optimization. The experiments were done using Lenovo S10 netbook and Nokia N900 smart phone.

Parallel Appearance-Adaptive Models for Real-Time Object Tracking Using Particle Swarm Optimization

Boguslaw Rymut and Bogdan Kwolek: Parallel Appearance-Adaptive Models for Real-Time Object Tracking Using Particle Swarm Optimization; Int. Conf. on Computational Collective Intelligence – Technologies and Applications, 2011, Computational Collective Intelligence. Technologies and Applications, Lecture Notes in Computer Science, Vol. 6923, Springer Berlin Heidelberg, 2011, pp. 455-464.

Abstract

This paper demonstrates how appearance adaptive models can be employed for real-time object tracking using particle swarm optimization. The parallelization of the code is done using OpenMP directives and SSE instructions. We show the performance of the algorithm that was evaluated on multi-core CPUs. Experimental results demonstrate the performance of the algorithm in comparison to our GPU based implementation of the object tracker using appearance-adaptive models. The algorithm has been tested on real image sequences.