Detection of abandoned objects in real time

Raheja, Jagdish Lal; Malireddy, Chaitanya; Singh, Aniket; Solanki, Lakhvinder Singh

doi:10.1109/icectech.2011.5941684

Cited by 3 publications

(5 citation statements)

References 8 publications

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“…Many of these approaches [39,77,92] directly use the original method proposed by Stauffer and Grimson, not applying any substantial modification to the original method and not specifying any parameter configuration. Other approaches propose the use of a specific number of Gaussian distributions [24] or alternative initialization processes [41].…”

Section: Gaussian Mixture Models (Gmm)mentioning

confidence: 99%

See 1 more Smart Citation

Detection of stationary foreground objects: A survey

Cuevas

Martínez

García

2016

Computer Vision and Image Understanding

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Section: Gaussian Mixture Models (Gmm)mentioning

confidence: 99%

“…Therefore, they apply some of the previously described algorithms to discard ROs from their final results. For example, in [73], the region growing analysis proposed in [58] is used for this purpose, whereas in [77] the ROs are discarded by applying an edge-based analysis.…”

Section: Ro Detectionmentioning

confidence: 99%

Detection of stationary foreground objects: A survey

Cuevas

Martínez

García

2016

Computer Vision and Image Understanding

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“…This idea was first proposed in [25] and, virtually simultaneously, also in [36] (with small differences between them). Later, it has been incorporated into other strategies [37]- [39] that are able to detect both totally and partially static foreground objects. Nevertheless, they are unable to detect long-term stationary foreground objects.…”

Section: Related Workmentioning

confidence: 99%

“…The first strategy (henceforth "Gaussian Stability (GS)") is based on an analysis of the stability of the Gaussians in a GMM associated to each pixel. This strategy was first proposed in [36] and, because of its computational efficiency and its high-quality results in many complex scenarios, it has been used by several authors over the past few years [37], [39]. The second strategy (henceforth "Dual Foreground Comparison (DFC)") is based on the dual foreground comparison proposed in [40], which has been taken as starting point by many recent works [43], [46].…”

Section: Experiments 1: Speed and Robustness Against Occlusionsmentioning

confidence: 99%

Detection of Stationary Foreground Objects Using Multiple Nonparametric Background-Foreground Models on a Finite State Machine

Cuevas

Martínez

Berjón

et al. 2017

IEEE Trans. on Image Process.

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There is a huge proliferation of surveillance systems that require strategies for detecting different kinds of stationary foreground objects (e.g., unattended packages or illegally parked vehicles). As these strategies must be able to detect foreground objects remaining static in crowd scenarios, regardless of how long they have not been moving, several algorithms for detecting different kinds of such foreground objects have been developed over the last decades. This paper presents an efficient and high-quality strategy to detect stationary foreground objects, which is able to detect not only completely static objects but also partially static ones. Three parallel nonparametric detectors with different absorption rates are used to detect currently moving foreground objects, short-term stationary foreground objects, and long-term stationary foreground objects. The results of the detectors are fed into a novel finite state machine that classifies the pixels among background, moving foreground objects, stationary foreground objects, occluded stationary foreground objects, and uncovered background. Results show that the proposed detection strategy is not only able to achieve high quality in several challenging situations but it also improves upon previous strategies.

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“…The HSV color space based skin filter was used to form the binary silhouette of the input image first. The hand segmentation was implemented as described in Raheja, Das, and Chaudhary (2011) to obtain the ROI. This color space separates three components: the hue (H), the saturation (S) and the brightness (V).…”

Section: Segmentaionmentioning

confidence: 99%

Both Hands’ Fingers’ Angle Calculation from Live Video

Chaudhary

Raheja

Das

et al. 2012

International Journal of Computer Vision and Image Processing

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In the last few years gesture recognition and gesture-based human computer interaction has gained a significant amount of popularity amongst researchers all over the world. It has a number of applications ranging from security to entertainment. Gesture recognition is a form of biometric identification that relies on the data acquired from the gesture depicted by an individual. This data, which can be either two-dimensional or three-dimensional, is compared against a database of individuals or is compared with respective thresholds based on the way of solving the riddle. In this paper, a novel method for angle calculation of both hands’ bended fingers is discussed and its application to a robotic hand control is presented. For the first time, such a study has been conducted in the area of natural computing for calculating angles without using any wired equipment, colors, marker or any device. The system deploys a simple camera and captures images. The pre-processing and segmentation of the region of interest is performed in a HSV color space and a binary format respectively. The technique presented in this paper requires no training for the user to perform the task.

show abstract

Detection of abandoned objects in real time

Cited by 3 publications

References 8 publications

Detection of stationary foreground objects: A survey

Detection of stationary foreground objects: A survey

Detection of Stationary Foreground Objects Using Multiple Nonparametric Background-Foreground Models on a Finite State Machine

Both Hands’ Fingers’ Angle Calculation from Live Video

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