Evolutionary adaptive eye tracking for low-cost human computer interaction applications

Shen, Yan; Shin, Hak Chul; Sung, Won Jun; Khim, Sarang; Kim, Honglak; Rhee, Phill Kyu

doi:10.1117/1.jei.22.1.013031

Cited by 5 publications

(8 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MDP model needs to justify the use of the RL approach for adaptive tracking optimization as the events of consecutive trials for deciding the MDP state observed in discrete time space. The RL approach relying on the Markov assumption is very useful in solving many real-world problems by approximating system behaviors and has been successfully applied [37]. The MDP formulized for adaptive visual tracking also relies on the assumption of the Markov property.…”

Section: Mdp For Optimal Visual Trackingmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive visual tracking using the prioritized Q-learning algorithm: MDP-based parameter learning approach

Khim

Hong

Kim

et al. 2014

Image and Vision Computing

Self Cite

View full text Add to dashboard Cite

a b s t r a c tThis paper introduces an adaptive visual tracking method that combines the adaptive appearance model and the optimization capability of the Markov decision process. Most tracking algorithms are limited due to variations in object appearance from changes in illumination, viewing angle, object scale, and object shape. This paper is motivated by the fact that tracking performance degradation is caused not only by changes in object appearance but also by the inflexible controls of tracker parameters. To the best of our knowledge, optimization of tracker parameters has not been thoroughly investigated, even though it critically influences tracking performance. The challenge is to equip an adaptive tracking algorithm with an optimization capability for a more flexible and robust appearance model. In this paper, the Markov decision process, which has been applied successfully in many dynamic systems, is employed to optimize an adaptive appearance model-based tracking algorithm. The adaptive visual tracking is formulated as a Markov decision process based dynamic parameter optimization problem with uncertain and incomplete information. The high computation requirements of the Markov decision process formulation are solved by the proposed prioritized Q-learning approach. We carried out extensive experiments using realistic video sets, and achieved very encouraging and competitive results.

show abstract

Section: Mdp For Optimal Visual Trackingmentioning

confidence: 99%

“…Tracking performance was evaluated using recall, precision, and harmonic mean [37]. Given an N image frame sequence in a video denoted as V = (I 1 , I 2 , …, I N ), the bounding box sequence of the ground truth object was compared with that of a tracked object.…”

Section: Performance Measurementioning

confidence: 99%

Adaptive visual tracking using the prioritized Q-learning algorithm: MDP-based parameter learning approach

Khim

Hong

Kim

et al. 2014

Image and Vision Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Their Euclidean distance of feature descriptor is less than 0.001 (the feature descriptors have been normalized into [0,1]). Their positions are (4,4) and (10,9) in ROI. ROI is divided into 16 × 16 grids.…”

Section: Performance Comparison Among Vq Svq Sc and Llcmentioning

confidence: 99%

“…[4][5][6] However, it still remains a challenging problem because of some factors such as camera motion, cluttered background, occlusion, and varied object appearance. Building a discriminative representation for actions in videos is still challenging for better recognition.…”

Section: Introductionmentioning

confidence: 99%

Position and locality constrained soft coding for human action recognition

Wang

Liu

Xiao

et al. 2013

J. Electron. Imaging

View full text Add to dashboard Cite

Although the traditional bag-of-words model has shown promising results for human action recognition, in the feature coding phase, the ambiguous features from different body parts are still difficult to distinguish. Furthermore, it also suffers from serious representation error. We propose an innovative coding strategy called position and locality constrained soft coding (PLSC) to overcome these limitations. PLSC uses the feature position in a human oriented region of interest (ROI) to distinguish the ambiguous features. We first construct a subdictionary for each feature by selecting the bases from their spatial neighbor in human ROI. Then, a modified soft coding with locality constraint is adopted to alleviate the quantization error and preserve the manifold structure of features. This novel coding algorithm increases both the representation accuracy and discriminative power with low computational cost. The human action recognition experimental results on KTH, Weizmann, and UCF sports datasets show that PLSC can achieve a better performance than previous competing feature coding methods.

show abstract

“…Finally, the tracking is performed by the partial Hough transform (PHT) and Kalman filter (KF). The more details can be found in [17] Let BN X be a parameter generator The interval of the threshold pivots between adjacent bit vector,…”

Section: The Genotype and Phenotype Leaning 41 Genotype Evolutionmentioning

confidence: 99%

Hybrid POMDP based evolutionary adaptive framework for efficient visual tracking algorithms

Shen

Khim

Sung

et al. 2013

Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation

Self Cite

View full text Add to dashboard Cite

This paper presents an evolutionary and adaptive framework for efficient visual tracking based on a hybrid POMDP formulation. The main focus is to guarantee visual tracking performance under varying environments without strongly-controlled situations or high-cost devices. The performance optimization is formulated as a dynamic adaptation of the system control parameters, i.e., threshold and adjusting parameters in a visual tracking algorithm, based on the hybrid of offline and online POMDPs. The hybrid POMDP allows the agent to construct world-belief models under uncertain environments in offline, and focus on the optimization of the system control parameters over the current world model in real-time. Since the visual tracking should satisfy strict real-time constraints, we restrict our attention to simpler and faster approaches instead of exploring the belief space of each world model directly. The hybrid POMDP is thus solved by an evolutionary adaptive framework employing the GA (Genetic Algorithm) and real-time Q-learning approaches in the optimally reachable genotype and phenotype spaces, respectively. Experiments were carried out extensively in the area of eye tracking using videos of various structures and qualities, and yielded very encouraging results. The framework can achieve an optimal performance by balancing the tracking accuracy and realtime constraints.

show abstract

Evolutionary adaptive eye tracking for low-cost human computer interaction applications

Cited by 5 publications

References 37 publications

Adaptive visual tracking using the prioritized Q-learning algorithm: MDP-based parameter learning approach

Adaptive visual tracking using the prioritized Q-learning algorithm: MDP-based parameter learning approach

Position and locality constrained soft coding for human action recognition

Hybrid POMDP based evolutionary adaptive framework for efficient visual tracking algorithms

Contact Info

Product

Resources

About