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International Journal of Computer Vision

Erschienen in:

04.10.2016

Partially Camouflaged Object Tracking using Modified Probabilistic Neural Network and Fuzzy Energy based Active Contour

verfasst von: Ajoy Mondal, Susmita Ghosh, Ashish Ghosh

Erschienen in: International Journal of Computer Vision | Ausgabe 1/2017

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Abstract

Various problems in object detection and tracking have attracted researchers to develop methodologies for solving these problems. Occurrence of camouflage is one of such challenges that makes object detection and tracking problems more complex. However, less attention has been given to detect and track camouflaged objects due to complexity of the problem. In this article, we propose a tracking-by-detection algorithm to detect and track camouflaged objects. To increase separability between the camouflaged object and the background, we propose to integrate features (CIELab, histogram of orientation gradients and locally adaptive ternary pattern) from multi-cue (color, shape and texture) to represent a camouflaged object. A probabilistic neural network (PNN) is modified to construct an efficient discriminative appearance model for detecting camouflaged objects in video sequences. A large number of training patterns (many could be redundant) are reduced based on motion of the object in the modified PNN. The modified PNN makes the detection process faster and also increases the detection accuracy. Due to high visual similarity among the camouflaged object and the background, the boundary of camouflaged object is not well defined (i.e., boundary may be smooth and/or discontinuous). In this context, a robust fuzzy energy based active contour model using both global and local information is proposed to extract contour (boundary) of the detected camouflaged object for tracking. We show a realization of the proposed method and demonstrate its performance (both quantitatively and qualitatively) with respect to state-of-the-art techniques on several challenging sequences. Analysis of results concludes that the proposed technique can track camouflaged (fully or partially) objects as well as objects in various complex environments in a better way as compare to the existing ones.

Vorheriger Artikel Semantic Decomposition and Recognition of Long and Complex Manipulation Action Sequences

Nächster Artikel Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose Estimation

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In this article, the words object and target are used interchangeably.

In this article, training, target, \((t-1)\mathrm{th}\) and previous frame are used interchangeably.

In this article, test, target candidate, \(t\mathrm{th}\) and current frame are used interchangeably.

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Titel: Partially Camouflaged Object Tracking using Modified Probabilistic Neural Network and Fuzzy Energy based Active Contour
verfasst von: Ajoy Mondal
Susmita Ghosh
Ashish Ghosh
Publikationsdatum: 04.10.2016
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 1/2017
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-016-0959-5

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