Body part boosting model for carried baggage detection and classification

Hariyono, Joko; Jo, Kang-Hyun

doi:10.1016/j.neucom.2016.10.038

Cited by 9 publications

(16 citation statements)

References 24 publications

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“…In this paper, as a fresh branch of active AVSS research, a contribution is deposited to automatic baggage detection and classification field. Among some successful baggage detection system, Wahyono, Haariyono and Jo [1] extracted scalable HOG descriptor, integrated boosting strategy into SVM and developed mixture model for baggage detection. They used two human body parameters to classify baggage and set those parameters depending on the head's height.…”

Section: Introductionmentioning

confidence: 99%

“…• The primary contribution of this paper is the detection of baggage having diverse texture patterns by using similarity based multilayer classifier and boosting technique. In this paper, to generate an identical feature for all baggage that is adorned by non-identical texture patterns in less time, LBP feature based multi layer SVMs have been boosted into RSD-HOG based SVM whereas mixture model in [1] introduced time complexity and high false alarm. • Moreover, another emphasis is given to set the human body parameter dynamically and make the parameter independent of head's height which is one of the limitations of [1].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, to generate an identical feature for all baggage that is adorned by non-identical texture patterns in less time, LBP feature based multi layer SVMs have been boosted into RSD-HOG based SVM whereas mixture model in [1] introduced time complexity and high false alarm. • Moreover, another emphasis is given to set the human body parameter dynamically and make the parameter independent of head's height which is one of the limitations of [1]. • Further, integration of rotation invariance nature into HOG using support plane dispel the narrowness of [1] and successfully detect uncarried and multiple pieces of carried baggage under the absence of human parts.…”

Section: Introductionmentioning

confidence: 99%

“…• Moreover, another emphasis is given to set the human body parameter dynamically and make the parameter independent of head's height which is one of the limitations of [1]. • Further, integration of rotation invariance nature into HOG using support plane dispel the narrowness of [1] and successfully detect uncarried and multiple pieces of carried baggage under the absence of human parts. • In addition, a simple mirroring algorithm is proposed using extrema points of ROI to detect occluded individual objects and accordingly, to set body parameter correctly.…”

Section: Introductionmentioning

confidence: 99%

“…• Furthermore, another attention is given to reduce computational time. Sliding window approach generated high computational time in [1] where background subtraction can reduce it. In this paper, a model using HSI model is proposed for background subtraction that is resistant against uneven illumination conditions.…”

Section: Introductionmentioning

confidence: 99%

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Baggage Recognition in Occluded Environment using Boosting Technique

2017

KSII TIIS

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Automatic Video Surveillance System (AVSS) has become important to computer vision researchers as crime has increased in the twenty-first century. As a new branch of AVSS, baggage detection has a wide area of security applications. Some of them are, detecting baggage in baggage restricted super shop, detecting unclaimed baggage in public space etc. However, in this paper, a detection & classification framework of baggage is proposed. Initially, background subtraction is performed instead of sliding window approach to speed up the system and HSI model is used to deal with different illumination conditions. Then, a model is introduced to overcome shadow effect. Then, occlusion of objects is detected using proposed mirroring algorithm to track individual objects. Extraction of rotational signal descriptor (SP-RSD-HOG) with support plane from Region of Interest (ROI) add rotation invariance nature in HOG. Finally, dynamic human body parameter setting approach enables the system to detect & classify single or multiple pieces of carried baggage even if some portions of human are absent. In baggage detection, a strong classifier is generated by boosting similarity measure based multi layer Support Vector Machine (SVM)s into HOG based SVM. This boosting technique has been used to deal with various texture patterns of baggage. Experimental results have discovered the system satisfactorily accurate and faster comparative to other alternatives.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Baggage Recognition in Occluded Environment using Boosting Technique

2017

KSII TIIS

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Human Carrying Baggage Classification Using Transfer Learning on CNN with Direction Attribute

Wahyono

2017

Intelligent Computing Theories and Application

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Gender discrimination, age group classification and carried object recognition from gait energy image using fusion of parallel convolutional neural network

Russel¹,

Arivazhagan²

2020

IET Image Processing

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Age and gender are the two key attributes for healthy social interactions, access control, intelligence marketing etc. Likewise, carried object recognition helps in identifying owner of the baggage being abandoned or the person littering in the public places. The above‐mentioned surveillance task displays discriminative characteristics in gait. Primates can accomplish scene context understanding and reacting to different circumstances with varying reflexes with ease. Human beings achieve this by recollecting prior experiences and adapting to new situations quickly. Modelling the human behaviour, this research work has combined customized and learnable filters so that knowledge database can always be kept up to date, as well as, provides flexibility in learning new contexts. Thus, a specialized parallel deep convolutional neural network architecture with customized filters that extracts intrinsic characteristics and data driven learnable filters are fused to enhance the performance of single convolutional neural network is proposed. From the experimentation it is observed that, the learning is augmented when customized filters and learnable filters are fused together. Results show that the proposed system achieves better performance for CASIA B datAQ2abase and OU‐ISIR gait database‐large population dataset with age and real‐life carried object.

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Body part boosting model for carried baggage detection and classification

Cited by 9 publications

References 24 publications

Baggage Recognition in Occluded Environment using Boosting Technique

Baggage Recognition in Occluded Environment using Boosting Technique

Human Carrying Baggage Classification Using Transfer Learning on CNN with Direction Attribute

Gender discrimination, age group classification and carried object recognition from gait energy image using fusion of parallel convolutional neural network

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