Human detection in video surveillance using texture features

Sabri, Nurbaity; İbrahim, Zarina Bibi; Saad, Mastura Md.; Mangshor, Nur Nabilah Abu; Jamil, Nursuriati

doi:10.1109/iccsce.2016.7893543

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…However, SIFT produce high accuracy only for simple and less noisy background images. Naï ve Bayes classifier produces a good result compared to SVM in human detection in video surveillance [13]. This classifier also achieved a high accuracy for human action recognition which is 99.4% [14].…”

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confidence: 90%

Evaluation of Basic Convolutional Neural Network, AlexNet and Bag of Features for Indoor Object Recognition

Zulkeflie¹,

Fammy²,

İbrahim³

et al. 2019

IJMLC

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This paper evaluates two deep learning techniques that are basic Convolutional Neural Network (CNN) and AlexNet along with a classical local descriptor that is Bag of Features (BoF) with Speeded-Up Robust Feature (SURF) and Support Vector Machine (SVM) classifier for indoor object recognition. A publicly available dataset, MCIndoor20000, has been used in this experiment that consists of doors, signage, and stairs images of Marshfield Clinic. Experimental results indicate that AlexNet achieves the highest accuracy followed by basic CNN and BoF. Furthermore, the results also show that BoF, a machine learning technique, can also produce a high accuracy performance as basic CNN, a deep learning technique, for image recognition.

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confidence: 90%

Evaluation of Basic Convolutional Neural Network, AlexNet and Bag of Features for Indoor Object Recognition

Zulkeflie¹,

Fammy²,

İbrahim³

et al. 2019

IJMLC

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, detecting humans at night conditions will be more difficult than during daytime conditions. This is due to several factors such as eccentric rays, silhouettes, and dim light [2]. This ability has real applications, such as smart-car, virtual reality, surveillance system, smart robots, and others.…”

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confidence: 99%

Convolutional Neural Network Using Kalman Filter for Human Detection and Tracking on RGB-D Video

Angelico

Wardani

2018

CommIT (Communication and Information Technology) Journal

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The computer ability to detect human being by computer vision is still being improved both in accuracy or computation time. In low-lighting condition, the detection accuracy is usually low. This research uses additional information, besides RGB channels, namely a depth map that shows objects’ distance relative to the camera. This research integrates Cascade Classifier (CC) to localize the potential object, the Convolutional Neural Network (CNN) technique to identify the human and nonhuman image, and the Kalman filter technique to track human movement. For training and testing purposes, there are two kinds of RGB-D datasets used with different points of view and lighting conditions. Both datasets have been selected to remove images which contain a lot of noises and occlusions so that during the training process it will be more directed. Using these integrated techniques, detection and tracking accuracy reach 77.7%. The impact of using Kalman filter increases computation efficiency by 41%.

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Analysis of Daubechies Wavelet Transform Based Human Detection Approaches in Digital Videos

Hemanth

Popescu

Anitha

2018

2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES)

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Human detection in video surveillance using texture features

Cited by 3 publications

References 34 publications

Evaluation of Basic Convolutional Neural Network, AlexNet and Bag of Features for Indoor Object Recognition

Evaluation of Basic Convolutional Neural Network, AlexNet and Bag of Features for Indoor Object Recognition

Convolutional Neural Network Using Kalman Filter for Human Detection and Tracking on RGB-D Video

Analysis of Daubechies Wavelet Transform Based Human Detection Approaches in Digital Videos

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