Into the woods: visual surveillance of noncooperative and camouflaged targets in complex outdoor settings

Boult, Terry; Micheals, Ross J.; Gao, Xiang; Eckmann, Michael

doi:10.1109/5.959337

Cited by 133 publications

(95 citation statements)

References 35 publications

(62 reference statements)

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“…a 360 degrees view in one single image). This kind of sensors has a lot of applications, such as video surveillance (Boult et al 2001) or object tracking (Chen et al 2008), and their use has become very common in robot navigation (Menegatti et al 2006) and in autonomous vehicles (Ehlgen et al 2008;Scaramuzza and Siegwart 2008). Interest points and local descriptors-based techniques, such as SIFT, have been applied to omnidirectional images due to their good performance in planar images (Goedeme et al 2005;Tamimi et al 2006;Valgren and Lilienthal 2007;Scaramuzza and Siegwart 2008).…”

Section: State-of-the-artmentioning

confidence: 99%

Scale Invariant Feature Transform on the Sphere: Theory and Applications

et al. 2011

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A SIFT algorithm in spherical coordinates for omnidirectional images is proposed. This algorithm can generate two types of local descriptors, Local Spherical Descriptors and Local Planar Descriptors. With the first ones, point matching between two omnidirectional images can be performed, and with the second ones, the same matching process can be done but between omnidirectional and planar images. Furthermore, a planar to spherical mapping is introduced and an algorithm for its estimation is given. This mapping allows to extract objects from an omnidirectional image given their SIFT descriptors in a planar image. Several

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Section: State-of-the-artmentioning

confidence: 99%

Scale Invariant Feature Transform on the Sphere: Theory and Applications

et al. 2011

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“…The trajectories were obtained by tracking the pedestrians using the LOTS algorithm [7]. In the synthetic example we assumed the number of fields to be known.…”

Section: Real Datamentioning

confidence: 99%

Efficient Optimization Algorithm for Space-Variant Mixture of Vector Fields

Nascimento

Barão

Marques

et al. 2013

Pattern Recognition and Image Analysis

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Abstract. This paper presents a new algorithm for trajectory classification of human activities. The presented framework uses a mixture of parametric space-variant vector fields to describe pedestrian's trajectories. An advantage of the proposed method is that the vector fields are not constant and depend on the pedestrian's localization. This means that the switching motion among vector fields may occur at any image location and should be accurately estimated. In this paper, the model is equipped with a novel methodology to estimate the switching probabilities among motion regimes. More specifically, we propose an iterative optimization of switching probabilities based on the natural gradient vector, with respect to the Fisher information metric. This approach follows an information geometric framework and contrasts with more traditional approaches of constrained optimization in which euclidean gradient based methods are used combined with probability simplex constraints. We testify the performance superiority of the proposed approach in the classification of pedestrian's trajectories in synthetic and real data sets concerning farfield surveillance scenarios.

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“…Many object tracking techniques focus on handling occlusions but neglect how to track slow moving or stopped objects. Boult et al [4] describe a system that performs well at detecting slow moving objects.…”

Section: Introductionmentioning

confidence: 99%

“…Detected objects are then tracked by a tracking module [1,4]. Most surveillance video analysis systems operate in a feed-forward manner to pass detections from background subtraction to the tracker and then tracks are stored or processed further, for instance by behavior analysis modules.…”

Section: Introductionmentioning

confidence: 99%

Interactive Motion Analysis for Video Surveillance and Long Term Scene Monitoring

Senior

Tian

2011

Computer Vision – ACCV 2010 Workshops

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Abstract. In video surveillance and long term scene monitoring applications, it is a challenging problem to handle slow-moving or stopped objects for motion analysis and tracking. We present a new framework by using two feedback mechanisms which allow interactions between tracking and background subtraction (BGS) to improve tracking accuracy, particularly in the cases of slow-moving and stopped objects. A publish-subscribe modular system that provides the framework for communication between components is described. The robustness and efficiency of the proposed method is tested on our real time video surveillance system. Quantitative performance evaluation is performed on a variety of sequences, including standard datasets. With the two feedback mechanisms enabled together, significant improvement in tracking performance are demonstrated particularly in handling slow moving and stopped objects.

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Into the woods: visual surveillance of noncooperative and camouflaged targets in complex outdoor settings

Abstract: Abstract

Cited by 133 publications

References 35 publications

Scale Invariant Feature Transform on the Sphere: Theory and Applications

Scale Invariant Feature Transform on the Sphere: Theory and Applications

Efficient Optimization Algorithm for Space-Variant Mixture of Vector Fields

Interactive Motion Analysis for Video Surveillance and Long Term Scene Monitoring

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