Automatic human activity recognition in video using background modeling and spatio-temporal template matching based technique

Sharma, Chandra Mani; Kushwaha, Alok Kr. Singh; Nigam, Swati; Khare, Ashish

doi:10.1145/2007052.2007072

Cited by 16 publications

(9 citation statements)

References 11 publications

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“…Also only motion of the object is taken into account to represent and recognize the different activities. The improvement over Bobick's approach was proposed by us in [9], where not only motion but also the shape of the actor is taken into account for creating spatio-temporal activities and background segmentation is performed with the help of a statistical model. The approach reports improvements over Bobick's method in terms of accuracy.…”

Section: Related Workmentioning

confidence: 99%

Human Action Recognition using Rule based Fuzzy Motion Feature Templates

Sharma*,

Ashok,

Kushwaha

2019

IJITEE

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Abstract: This paper proposes a technique for human activity recognition in a video stream. To achieve high accuracy in activity recognition results, the method in its initial step deploys temporal template matching to recognize activities. As temporal templates are susceptible to get affected by speed, style and performance pattern of activity, so it becomes difficult to accurately differentiate among closely similar activities (e.g walking, running and jogging). The confusion in recognizing activities is reconciled by subsequent rule based activity distinction. The proposed method recognizes the human activities in video on various bench-marked data sets including KTH Dataset and Weizmann Dataset. Experimental results demonstrate the novelty of method with a wide spectrum of varied conditions. The average accuracy of the method is 97.20% under standard conditions.

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Section: Related Workmentioning

confidence: 99%

Human Action Recognition using Rule based Fuzzy Motion Feature Templates

Sharma*,

Ashok,

Kushwaha

2019

IJITEE

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“…Human action recognition is a challenging zone of research because of its various potential applications in visual surveillance. Automatic human activity recognition in video using background modelling and spatio-temporal template matching based technique [28] can be proposed by Chandra Mani Sharm. This activity recognition processed based on the spatiotemporal template matching.…”

Section: Description and Behaviour Understandingmentioning

confidence: 99%

“…But the proposed method accurately measures these activities. Automatic human activity recognition in video using background modeling and spatio-temporal template matching based technique [28].…”

Section: Description and Behaviour Understandingmentioning

confidence: 99%

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A Review of Human Activity Recongnition and Behavior Understanding in Video Surveillance

Revathi¹

2012

Computer Science &Amp; Information Technology (CS &Amp; IT)

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This paper presents a survey of activity recognition and understanding the behaviour of human activity in video sequence. The major goal of this paper is to provide a general review on the overall process of a surveillance system used in the current trend. Visual surveillance system is directed on automatic identification of events of interest, especially on tracking and classification of moving objects. The processing step of the video surveillance system includes the following stages: Surrounding model, object representation, object tracking, activity recognition and behaviour understanding. It describes techniques that use to define a general set of activities that are applicable to a wide range of scenes and environments in video sequence. The review methods used for real-time surveillance through a set of events for further analysis triggering, including virtual fencing, speed profiling, behaviour classification, anomaly detection, and object interaction.

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“…It is the foundation of several applications like object tracking [1][2], activity recognition [3][4], etc. A number of methods have been proposed so far for object detection purpose.…”

Section: Introductionmentioning

confidence: 99%

An effective local feature descriptor for object detection in real scenes

Nigam

Khare

Srivastava

et al. 2013

2013 Ieee Conference on Information and Communication Technologies

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In this study, we advocate the importance of robust local features that allow object form to be distinguished from other objects for detection purpose. We start from the grid of Histogram of oriented gradients (HOG) and integrate Scale Invariant Feature Transform (SIFT) within them. In HOG features an object's appearance is detected by the distribution of local intensity gradients or edge directions for different cells. In the proposed method we have computed the SIFT despite of computing intensity gradients for these cells. In this way, the proposed approach does not only provide more significant information than just providing intensity gradients but also proves to deal with following challenges: (i) scale invariance; (ii) rotation invariance; (iii) change in illumination; and (iv) change in view points. With qualitative and quantitative experimental evaluation on standard INRIA dataset, we have compared the proposed method with other state of the art object detection methods and demonstrated better performance over them.

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Automatic human activity recognition in video using background modeling and spatio-temporal template matching based technique

Cited by 16 publications

References 11 publications

Human Action Recognition using Rule based Fuzzy Motion Feature Templates

Human Action Recognition using Rule based Fuzzy Motion Feature Templates

A Review of Human Activity Recongnition and Behavior Understanding in Video Surveillance

An effective local feature descriptor for object detection in real scenes

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