Visual Saliency Based Object Tracking

Zhang, Geng; Yuan, Zejian; Zheng, Nanning; Sheng, Xingdong; Liu, Tie

doi:10.1007/978-3-642-12304-7_19

Cited by 34 publications

(19 citation statements)

References 13 publications

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“…While psychologists and physiologists are interested in human visual attention behavior and anatomical evidence to support attention theory, computer scientists are concentrating on building computational models of visual attention that implement visual saliency in computers or machines. Computational visual attention has many applications for computer vision tasks, such as robot localization (Shubina & Tsotsos, 2010;Siagian & Itti, 2009), object tracking (G. Zhang, Yuan, Zheng, Sheng, & Liu, 2010), image/video compression (Guo & Zhang, 2010;Itti, 2004), object detection (Frintrop, 2006;Liu et al, 2011), image thumbnailing (Le Meur, Le Callet, Barba, & Thoreau, 2006;Marchesotti, Cifarelli, & Csurka, 2009), and implementation of smart cameras (Casares, Velipasalar, & Pinto, 2010).…”

Section: Introductionmentioning

confidence: 99%

A computational visual saliency model based on statistics and machine learning

Lin

2014

Journal of Vision

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Identifying the type of stimuli that attracts human visual attention has been an appealing topic for scientists for many years. In particular, marking the salient regions in images is useful for both psychologists and many computer vision applications. In this paper, we propose a computational approach for producing saliency maps using statistics and machine learning methods. Based on four assumptions, three properties (Feature-Prior, Position-Prior, and Feature-Distribution) can be derived and combined by a simple intersection operation to obtain a saliency map. These properties are implemented by a similarity computation, support vector regression (SVR) technique, statistical analysis of training samples, and information theory using low-level features. This technique is able to learn the preferences of human visual behavior while simultaneously considering feature uniqueness. Experimental results show that our approach performs better in predicting human visual attention regions than 12 other models in two test databases.

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Section: Introductionmentioning

confidence: 99%

A computational visual saliency model based on statistics and machine learning

Lin

2014

Journal of Vision

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“…In [44], the authors developed a bottom-up saliency tracker that tracked any salient target in the scene, which was done in an entirely unsupervised manner. This is in contrast to the typical online tracking problem, in which a particular target is to be followed throughout a video.…”

Section: Related Deep Convnet and Saliency Trackersmentioning

confidence: 99%

Online tracking using saliency

Yousefhussien

Browning

Kanan

2016

2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

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When tracking small moving objects, primates use smooth pursuit eye movements to keep a target in the center of the field of view. In this paper, we propose the Smooth Pursuit tracking algorithm, which uses three kinds of saliency maps to perform online target tracking: appearance, location, and motion. In addition to tracking single targets, our method can track multiple targets with little additional overhead. The appearance saliency map uses deep convolutional neural network features along with gnostic fields, a brain-inspired model for object recognition. The location saliency map predicts where the object will move next. Finally, the motion saliency map indicates which objects are moving in the scene. We combine all three saliency maps into a smooth pursuit map, which is used to generate bounding boxes for tracked objects. We evaluate our algorithm and others from the literature on a vehicle tracking task. Our approach achieves the best overall performance, including being the only method we tested capable of handling long-term occlusions.

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“…7-9), and they have been used to address problems such as human scene analysis, 10 video compression, 11 and object tracking. 12 Saliency maps from different labs differ in how they incorporate image and observer characteristics (e.g., color and intensity contrast, 13 visual motion, 14 and biological models 15 ). Comparing the success with which various salience models predict eye movements is one source of information describing the image information that is used in observers' search strategies.…”

Section: Introductionmentioning

confidence: 99%

Computational assessment of visual search strategies in volumetric medical images

et al. 2016

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Abstract. When searching through volumetric images [e.g., computed tomography (CT)], radiologists appear to use two different search strategies: "drilling" (restrict eye movements to a small region of the image while quickly scrolling through slices), or "scanning" (search over large areas at a given depth before moving on to the next slice). To computationally identify the type of image information that is used in these two strategies, 23 naïve observers were instructed with either "drilling" or "scanning" when searching for target T's in 20 volumes of faux lung CTs. We computed saliency maps using both classical two-dimensional (2-D) saliency, and a three-dimensional (3-D) dynamic saliency that captures the characteristics of scrolling through slices. Comparing observers' gaze distributions with the saliency maps showed that search strategy alters the type of saliency that attracts fixations. Drillers' fixations aligned better with dynamic saliency and scanners with 2-D saliency. The computed saliency was greater for detected targets than for missed targets. Similar results were observed in data from 19 radiologists who searched five stacks of clinical chest CTs for lung nodules. Dynamic saliency may be superior to the 2-D saliency for detecting targets embedded in volumetric images, and thus "drilling" may be more efficient than "scanning."

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Visual Saliency Based Object Tracking

Cited by 34 publications

References 13 publications

A computational visual saliency model based on statistics and machine learning

A computational visual saliency model based on statistics and machine learning

Online tracking using saliency

Computational assessment of visual search strategies in volumetric medical images

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