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Video segmentation is a stepping stone to understanding video context. Video segmentation enables one to represent a video by decomposing it into coherent regions which comprise whole or parts of objects. However, the challenge originates from the fact that most of the video segmentation algorithms are based on unsupervised learning due to expensive cost of pixelwise video annotation and intra-class...
Smartphone based indoor localization caught massive interest of the localization community in recent years. Combining pedestrian dead reckoning obtained using the phone's inertial sensors with the Graph SLAM (Simultaneous Localization and Mapping) algorithm is one of the most effective approaches to reconstruct the entire pedestrian trajectory given a set of visited landmarks during movement. A key...
We propose an autocorrelation Cox process that extends the traditional bag-of-words representation to model the spatio-temporal context within a video sequence. Bag-of-words models are effective tools for representing a video by a histogram of visual words that describe local appearance and motion. A major limitation of this model is its inability to encode the spatio-temporal structure of visual...
Indoor localization typically relies on measuring a collection of RF signals, such as Received Signal Strength (RSS) from WiFi, in conjunction with spatial maps of signal fingerprints. A new technology for localization could arise with the use of 4G LTE telephony small cells, with limited range but with rich signal strength information, namely Reference Signal Received Power (RSRP). In this paper,...
Basic understanding and recognition of human actions can be accomplished by modeling the spatiotemporal relationship among major skeletal joints. In this work we present an approach that models human actions using temporal causal relations of joint movements. The relations form a graph with joints as nodes and edges induced by the Granger causality measure between pairs of joint point processes. Each...
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