My name is Ivan Bogun. I am 4nd year PhD student working towards my degree in Computer Science department at FIT under supervision of Dr. Eraldo Ribeiro. My research interests include application of sparsity seeking convex optimization problems to computer vision and machine learning. Recently, I've been interested in model-free tracking. My free time I like to spend practicing guitar, playing video games or taking courses on Coursera.
In this paper we extend a tracker, based on Structured SVM, known as Struck to output bounding boxes on multiple scales. Furthermore, to decrease the possibility of false negative detection and in order to make the tracker resilient towards short time occlusions we spatially smooth results of the tracking with Robust Kalman filter. A special strategy is developed for the tracker update designed to decrease overfitting and to allow for the tracker to reacquire lost track. We thoroughly evaluate the method and perform sensitivity analysis on different benchmarks with different evaluation protocols. Our results show that our method establishes new state-of-the-art on both datasets
We build a network of words based on the “Captain Phillips” movie tweets. A separate network was built for each week tweets were downloaded ( in total 4 weeks). We show that all resulting networks are scale-free. We apply sentiment analysis to determine movie ratings. Sentiment analyses is extended to network communities. Finally, we show how community sentiments can be used to track their “positiveness”.
In this paper we investigate the problem of human-object interaction recognition. Using trajectories information alone we propose an unsupervised framework for interaction clustering. Our method is based on the algorithm , first developed for motion segmentation. We show that each interaction can be seen as a trajectory laying on low- dimensional space and that subspace clustering is able to recover them. Experimental results, performed on the Gupta dataset , show that our approach is comparable to the state of the art .