IJETSI

Title: DETECTION AND TRACKING OF PEDESTRIAN CROSSWALKS USING RECURRENT NEURAL NETWORK CLASSIFIER AND HISTOGRAMS OF ORIENTED GRADIENTS IN THE INFRARED IMAGE SEQUENCE
Authors: SEYED MOHAMMAD SHAHROKHI , MOHAMMADREZA AMINI
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SEYED MOHAMMAD SHAHROKHI1 , MOHAMMADREZA AMINI2 1,2. Department of Electrical Engineering, Borujerd Branch, Islamic Azad University, Borujerd, Iran
MLA 8 SHAHROKHI, SEYED MOHAMMAD, and MOHAMMADREZA AMINI. "DETECTION AND TRACKING OF PEDESTRIAN CROSSWALKS USING RECURRENT NEURAL NETWORK CLASSIFIER AND HISTOGRAMS OF ORIENTED GRADIENTS IN THE INFRARED IMAGE SEQUENCE." IJETSI, vol. 5, no. 1, Jan.-Feb. 2020, pp. 12-22, ijetsi.org/more2020.php?id=2. Accessed Jan.-Feb. 2020. APA(6) SHAHROKHI, S., & AMINI, M. (2020, January/February). DETECTION AND TRACKING OF PEDESTRIAN CROSSWALKS USING RECURRENT NEURAL NETWORK CLASSIFIER AND HISTOGRAMS OF ORIENTED GRADIENTS IN THE INFRARED IMAGE SEQUENCE. IJETSI, 5(1), 12-22. Retrieved from ijetsi.org/more2020.php?id=2 Chicago SHAHROKHI, SEYED MOHAMMAD, and MOHAMMADREZA AMINI. "DETECTION AND TRACKING OF PEDESTRIAN CROSSWALKS USING RECURRENT NEURAL NETWORK CLASSIFIER AND HISTOGRAMS OF ORIENTED GRADIENTS IN THE INFRARED IMAGE SEQUENCE." IJETSI 5, no. 1 (January/February 2020), 12-22. Accessed January/February, 2020. ijetsi.org/more2020.php?id=2.
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Abstract: Recently, detection of pedestrian crosswalks has been identified as one of the important issues in the field of image processing and statistical identification. The simultaneous detection and tracking of pedestrians are much important, however, there are challenges such as timeconsuming and uncertainty in determining the position of a person. In the past, automated methods have been proposed that often have low accuracy and uncertainty in achieving an optimal response. These methods lack comprehensiveness and suffer over fitting problem. In this paper, an algorithm is proposed for the automatic detecting of the pedestrians' position and tracking based on an efficient approach consisting of recurrent neural networks and the gradient histogram in infrared images, which is more accurate and faster than the similar methods. In the first step, the best features are selected by the gradient histogram algorithm and then detection and tracking will be done in the recurrent neural network algorithm. A K-fold validation technique is used to divide the training and test data with a value of K equals to 10. The proposed algorithm has an acceptable performance with an error of less than 5% in detecting and tracking pedestrians.

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