Top–down visual attention integrated particle filter for robust object tracking

Robust object tracking guided by top-down spectral analysis visual attention

Neurocomputing ◽

10.1016/j.neucom.2014.11.006 ◽

2015 ◽

Vol 152 ◽

pp. 170-178 ◽

Cited By ~ 10

Author(s):

Wanyi Li ◽

Peng Wang ◽

Rui Jiang ◽

Hong Qiao

Keyword(s):

Spectral Analysis ◽

Visual Attention ◽

Object Tracking ◽

Top Down

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A SINGLE-OBJECT TRACKING METHOD FOR ROBOTS USING OBJECT-BASED VISUAL ATTENTION

International Journal of Humanoid Robotics ◽

10.1142/s0219843612500302 ◽

2012 ◽

Vol 09 (04) ◽

pp. 1250030 ◽

Cited By ~ 5

Author(s):

YUANLONG YU ◽

GEORGE K. I. MANN ◽

RAYMOND G. GOSINE

Keyword(s):

Online Learning ◽

Visual Attention ◽

Object Tracking ◽

Single Object ◽

Top Down ◽

Target Region ◽

Tracking Method ◽

Discriminative Feature ◽

Object Based ◽

Processing Module

It is a quite challenging problem for robots to track the target in complex environment due to appearance changes of the target and background, large variation of motion, partial and full occlusion, motion of the camera and so on. However, humans are capable to cope with these difficulties by using their cognitive capability, mainly including the visual attention and learning mechanisms. This paper therefore presents a single-object tracking method for robots based on the object-based attention mechanism. This tracking method consists of four modules: pre-attentive segmentation, top-down attentional selection, post-attentive processing and online learning of the target model. The pre-attentive segmentation module first divides the scene into uniform proto-objects. Then the top-down attention module selects one proto-object over the predicted region by using a discriminative feature of the target. The post-attentive processing module then validates the attended proto-object. If it is confirmed to be the target, it is used to obtain the complete target region. Otherwise, the recovery mechanism is automatically triggered to globally search for the target. Given the complete target region, the online learning algorithm autonomously updates the target model, which consists of appearance and saliency components. The saliency component is used to automatically select a discriminative feature for top-down attention, while the appearance component is used for bias estimation in the top-down attention module and validation in the post-attentive processing module. Experiments have shown that this proposed method outperforms other algorithms without using attention for tracking a single target in cluttered and dynamically changing environment.

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