Robust Visual Tracking with SIFT Features and Fragments Based on Particle Swarm Optimization

Cheng, Xu; Li, Nijun; Zhang, Suofei; Wu, Zhenyang

doi:10.1007/s00034-013-9713-1

Cited by 15 publications

(12 citation statements)

References 22 publications

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“…A summary of them can be seen in Table 1. The i3DPost Multi-View Dataset [31] was recorded using a convergent eight camera setup to produce high definition multi-view videos, where each video depicts one of eight people performing one of twelve different human actions. A subset for gait recognition can be obtained from this dataset.…”

Section: Current Datasets For Gait Recognitionmentioning

confidence: 99%

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Activity Monitoring by Multiple Distributed Sensing

Mazzeo

Spagnolo

Moeslund³

2014

Lecture Notes in Computer Science

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In this paper, a topological approach for monitoring human activities is presented. This approach makes possible to protect the person's privacy hiding details that are not essential for processing a security alarm. First, a stack of human silhouettes, extracted by background subtraction and thresholding, are glued through their gravity centers, forming a 3D digital binary image I. Secondly, different orders of the simplices are applied on a simplicial complex obtained from I, which capture relations among the parts of the human body when walking. Finally, a topological signature is extracted from the persistence diagrams according to each order. The measure cosine is used to give a similarity value between topological signatures. In this way, the powerful topological tool known as persistent homology is novelty adapted to deal with gender classification, person identification, carrying bag detection and simple action recognition. Four experiments show the strength of the topological feature used; three of they use the CASIA-B database, and the fourth use the KTH database to present the results in the case of simple actions recognition. In the first experiment the named topo-logical signature is evaluated, obtaining 98.8 % (lateral view) of correct classification rates for gender identification. In the second one are shown results for person identification, obtaining an average of 98.5 %. In the third one the result obtained is 93.8 % for carrying bag detection. And in the last experiment the results were 97.7 % walking and 97.5 % running, which were the actions took from the KTH database.

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Section: Current Datasets For Gait Recognitionmentioning

confidence: 99%

“…Instead of using a screen backdrop of a specific color, as in [31], the background of the scene is the white wall of the studio. However, to facilitate foreground segmentation, the actors wear clothes of different color than the background scene.…”

Section: Studio Environment and Camera Setupmentioning

confidence: 99%

Activity Monitoring by Multiple Distributed Sensing

Mazzeo

Spagnolo

Moeslund³

2014

Lecture Notes in Computer Science

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“…Accurate tracking is crucial for many applications, such as pedestrian safety, motion and scene analysis, and video surveillance [1]. To address this problem, several methods [2][3][4][5] have proposed to integrate the social interactions among targets in the tracking algorithms. Despite enormous progress in recent years, the tracking abilities of humans still easily exceed state-of-the-art algorithms in real world scenarios, both in terms of precision and accuracy.…”

Section: Introductionmentioning

confidence: 99%

A multi-swarm particle swarm optimization algorithm for tracking multiple targets

Zheng

Jie

Hou

et al. 2014

2014 9th IEEE Conference on Industrial Electronics and Applications

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A nove multi-swarm particle swarm optimization algorithm to tracking multiple targets is proposed. The global swarm is divided into many sub-swarms according to the number of targets, and each sub-swarm searches for its target and maintains track of it. The information interaction between different sub-swarm is described as a differentiable energy function, which include an observation model and a mutual exclusion model. Experimental results demonstrate the efficiency of our method.

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“…David et al [3] proposed an efficient method which learned a linear subspace online to model the variations of object appearance. In [4], an efficient tracker with the SIFT feature correspondence and multiple fragments was used to track the object. Re- cently, sparse representation has attracted considerable interest in object tracking [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Robust Superpixel Tracking with Weighted Multiple-Instance Learning

Cheng

Zhou

et al. 2015

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper proposes a robust superpixel-based tracker via multiple-instance learning, which exploits the importance of instances and mid-level features captured by superpixels for object tracking. We first present a superpixels-based appearance model, which is able to compute the confidences of the object and background. Most importantly, we introduce the sample importance into multiple-instance learning (MIL) procedure to improve the performance of tracking. The importance for each instance in the positive bag is defined by accumulating the confidence of all the pixels within the corresponding instance. Furthermore, our tracker can help recover the object from the drifting scene using the appearance model based on superpixels when the drift occurs. We retain the first (k − 1) frames' information during the updating process to alleviate drift to some extent. To evaluate the effectiveness of the proposed tracker, six video sequences of different challenging situations are tested. The comparison results demonstrate that the proposed tracker has more robust and accurate performance than six ones representing the state-of-the-art.

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Robust Visual Tracking with SIFT Features and Fragments Based on Particle Swarm Optimization

Cited by 15 publications

References 22 publications

Activity Monitoring by Multiple Distributed Sensing

Activity Monitoring by Multiple Distributed Sensing

A multi-swarm particle swarm optimization algorithm for tracking multiple targets

Robust Superpixel Tracking with Weighted Multiple-Instance Learning

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