Hoang Thanh Nguyen

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Human-activity recognition is one of the most challenging problems in computer vision. Researchers from around the world have tried to solve this problem and have come a long way in recognizing simple motions and atomic activities. As the computer vision community heads toward fully recognizing human activities, a challenging and labeled dataset is needed.(More)
Sensor networks have been a very active area of research in recent years. However, most of the sensors used in the development of these networks have been local and non-imaging sensors such as acoustics, seismic, vibration, temperature, humidity, etc. The development of emerging video sensor networks poses its own set of unique challenges, including high(More)
—Pedestrian tracking is an important problem with many practical applications in fields such as security, animation, and human computer interaction (HCI). In this paper, we introduce a previously-unexplored swarm intelligence approach to multi-object monocular tracking by using Bacterial Foraging Optimization (BFO) swarms to drive a novel part-based(More)
Sensor networks have been a very active area of research in recent years. However, most of the sensors used in the development of these networks have been local and nonimaging sensors such as acoustics, seismic, vibration, temperature, humidity. The emerging development of video sensor networks poses its own set of unique challenges, including(More)
One of the key problems in computer vision and pattern recognition is tracking. Multiple objects, occlusion, and tracking moving objects using a moving camera are some of the challenges that one may face in developing an effective approach for tracking. While there are numerous algorithms and approaches to the tracking problem with their own shortcomings, a(More)
In this paper, we present swarm intelligence algorithms for pedestrian tracking. In particular, we present a modified Bacterial Foraging Optimization (BFO) algorithm and show that it outperforms PSO in a number of important met-rics for pedestrian tracking. In our experiments, we show that BFO's search strategy is inherently more efficient than PSO under a(More)
OBJECTIVE Health disparities in access to care, early detection, and survival exist among adult patients with cancer. However, there have been few reports assessing how health disparities impact pediatric patients with malignancies. The objective in this study was to examine the impact of racial/ethnic and social factors on disease presentation and outcome(More)
INTRODUCTION Regular physical activity (PA) can improve health outcomes in cancer survivors, but the rate of adherence to PA recommendations among middle-aged survivors is unclear. We examined adherence to PA recommendations among cancer survivors and controls. We sought to identify correlates of adherence to PA and to determine whether PA adherence is(More)
Search optimization algorithms have the challenge of balancing between exploration of the search space (e.g., map locations, image pixels) and exploitation of learned information (e.g., prior knowledge, regions of high fitness). To address this challenge, we present a very basic framework which we call Zombie Survival Optimization (ZSO), a novel swarm(More)