Sling bag and backpack detection for human appearance semantic in vision system

Chua, Teck Wee; Leman, Karianto; Wang, Hee Lin; Pham, Nam Trung; Chang, Richard; Nguyen, Dinh Duy; Zhang, Jie

doi:10.1109/iros.2013.6696654

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…The body-part method spatially localizes the CO area at a specific location based on body proportions and camera position [6]. Various kinds of CO can be detected by utilizing the general position of the CO carried based on the bend line.…”

Section: G Body-partmentioning

confidence: 99%

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Backpack detection model using multi-scale superpixel and body-part segmentation

Hidayat,

Harjoko,

Musdholifah

2023

International Journal on Smart Sensing and Intelligent Systems

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A backpack is a type of carried object (CO) widely used for various purposes because of its practicality. Various valuable items such as wallets, laptops, cameras, and cellphones may be kept in backpacks. Detecting backpacks in video surveillance is challenging due to their varying shapes, sizes, and colors. The process of localizing the area of the backpack in the image is a critical stage and dramatically influences the success of detection. This paper focuses on the process of localizing the backpack area through a multi-scale segmentation approach, where different scales are intended to detect the various size of the backpacks. Based on the assumption that the backpack is generally located above the bend line, the body-part method is then used to select superpixels. The selected superpixel feature is then extracted and used to train the model. Model testing is carried out in two scenarios. In the first scenario, the model is tested using the HOG (histogram of oriented gradients) feature, while in the second scenario, the model is tested using a combination of the HOG and histogram features. The experiment results show that on the DIKE20 dataset, the proposed model obtained an average F1 score of 69%. On PETS2006 and i-LIDS datasets, the proposed model shows an average F1 score of 68%, better than the average F1 score obtained by the state-of-the-art method.

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Section: G Body-partmentioning

confidence: 99%

“…In addition, backpacks are often confused for one another because many look similar. Research that developed a backpack detection model in video surveillance was reported in [6]- [9]. However, the performance of these methods still needs to be improved so that they can be widely used [10].…”

Section: Introductionmentioning

confidence: 99%

Backpack detection model using multi-scale superpixel and body-part segmentation

Hidayat,

Harjoko,

Musdholifah

2023

International Journal on Smart Sensing and Intelligent Systems

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“…In Branca et al [1] work, two classifiers are trained to detect two types of tools that are often used by intruders on archaeological sites. They use the wavelet transform coefficients of a binary foreground blob as features to feed the classifier and search around a person body to detect COs. Chua et al [13] detected sling bag and backpack using their geometrical shape cues (e.g., sling bag straps can be described as two near-parallel lines). Yue et al [14] modeled the spatial relation of points on the person contour to the person main axis using Support Vector Machine (SVM) to classify backpacks and luggage.…”

Section: Related Workmentioning

confidence: 99%

From superpixel to human shape modelling for carried object detection

Ghadiri

Bergevin

Bilodeau

2019

Pattern Recognition

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Detecting carried objects is one of the requirements for developing systems to reason about activities involving people and objects. We present an approach to detect carried objects from a single video frame with a novel method that incorporates features from multiple scales. Initially, a foreground mask in a video frame is segmented into multi-scale superpixels. Then the human-like regions in the segmented area are identified by matching a set of extracted features from superpixels against learned features in a codebook. A carried object probability map is generated using the complement of the matching probabilities of superpixels to human-like regions and background information. A group of superpixels with high carried object probability and strong edge support is then merged to obtain the shape of the carried object. We applied our method to two challenging datasets, and results show that our method is competitive with or better than the state-of-the-art.

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“…Until now different methods of carrying object detection have been proposed. One of them (1) defines geometrical properties of backpack and sling bag strap with Hough transform and edge detection method. In this method, geometrical properties are separately calculated.…”

Section: Related Workmentioning

confidence: 99%

Real Time Backpack Detection in Visual Surveillance based on Verticals Contour Analysis

Lkhagvasuren

Karungaru

Terada

2018

Proceedings of the 6th IIAE International Conference on Industrial Application Engineering 2018

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In this paper, we aim to propose a method to detect backpacks and sling bags which pedestrians are carrying in visual surveillance. A real time video analysis and processing on passive recording provided from surveillance camera could give more valuable information for a variety of applications. We detect backpacks without protrusion supposition or any training data. We propose an algorithm to find expectable vertical lines inside or near a human body contours using some fuzzy rules. To improve detection accuracy, area of carried object is compared with next several frames. It influences our detection result greatly. As a result, we detect carried objects including backpack and sling bag successfully. We run several experiments with previously designed VIRAT ground video dataset and result shows that our method is promising.

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Sling bag and backpack detection for human appearance semantic in vision system

Cited by 3 publications

References 15 publications

Backpack detection model using multi-scale superpixel and body-part segmentation

Backpack detection model using multi-scale superpixel and body-part segmentation

From superpixel to human shape modelling for carried object detection

Real Time Backpack Detection in Visual Surveillance based on Verticals Contour Analysis

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