Unsupervised Feature Descriptors Based Facial Tracking over Distributed Geospatial Subspaces
05 Dec 2017-pp 196-202
TL;DR: This work proposes a system for fast large scale facial tracking over distributed systems beyond individual human capabilities leveraging the computational prowess of large scale processing engines such as Apache Spark.
Abstract: Object Tracking has primarily been characterized as the study of object motion trajectory over constraint subspaces under attempts to mimic human efficiency. However, the trend of monotonically increasing applicability and integrated relevance over distributed commercial frontiers necessitates that scalability be addressed. The present work proposes a system for fast large scale facial tracking over distributed systems beyond individual human capabilities leveraging the computational prowess of large scale processing engines such as Apache Spark. The system is pivoted on an interval based approach for receiving the input feed streams, which is followed by a deep encoder-decoder network for generation of robust environment invariant feature encoding. The system performance is analyzed while functionally varying various pipeline components, to highlight the robustness of the vector representations and near real-time processing performance.
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01 Dec 2013
TL;DR: A distributed camera and processing based face detection and recognition system which can generate information for finding spatiotemporal movement pattern of individuals over a large monitored space is proposed.
Abstract: Large space with many cameras require huge storage and computational power to process these data for surveillance applications. In this paper we propose a distributed camera and processing based face detection and recognition system which can generate information for finding spatiotemporal movement pattern of individuals over a large monitored space. The system is built upon Hadoop Distributed File System using map reduce programming model. A novel key generation scheme using distance based hashing technique has been used for distribution of the face matching task. Experimental results have established effectiveness of the technique.
11 citations
"Unsupervised Feature Descriptors Ba..." refers background in this paper
...These have largely been holistic or patch based [8]....
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14 Dec 2014
TL;DR: It is shown that objects can be successfully tracked across videos, under challenging conditions such as scale variations, illumination changes and occlusion using the proposed technique.
Abstract: This paper proposes a novel algorithm for object tracking using Approximate Nearest Neighbour Fields (ANNF). ANNF maps have been previously used to address several problems like denoising, image completion, re-targeting and medical image analysis. In this paper, we deal with the challenging problem of visual object tracking, using patch flow. The proposed method uses FeatureMatch to find patch correspondence between successive frames, enabling the tracker to find the best match for the object in the next frame. Based on the flow, each patch is labeled as either foreground or background. The proportion of FG/BG/border patches contributing to each pixel determines its final label. We show that objects can be successfully tracked across videos, under challenging conditions such as scale variations, illumination changes and occlusion using the proposed technique.
1 citations
"Unsupervised Feature Descriptors Ba..." refers background in this paper
...Several near real-time systems such as A Real-time face tracker [11], Pfinder [10], patch flow based [9] have been researched and reported with attempts to achieve human like accuracy in effortlessly tracking objects of interest....
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...Object tracking has largely been characterised and defined as the problem of estimating the trajectory of a moving object [9] over constrained subspace....
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