Adaptive Pedestrian Detection in Infrared Images Using Background Subtraction and Local Thresholding

Soundrapandiyan, Rajkumar; Mouli, P. V. S. S. R. Chandra

doi:10.1016/j.procs.2015.08.091

Cited by 25 publications

(12 citation statements)

References 25 publications

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“…Human detection and tracking using stationary thermal imaging cameras have been studied in [18][19][20][21][22]. A contour-based background-subtraction to extract foreground objects was presented [18].…”

Section: Introductionmentioning

confidence: 99%

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Moving People Tracking and False Track Removing with Infrared Thermal Imaging by a Multirotor

Yeom

2021

Drones

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Infrared (IR) thermal imaging can detect the warm temperature of the human body regardless of the light conditions, thus small drones equipped with the IR thermal camera can be utilized to recognize human activity for smart surveillance, road safety, and search and rescue missions. However, the unpredictable motion of the drone poses more challenges than a fixed camera. This paper addresses the detection and tracking of people through IR thermal video captured by a multirotor. For object detection, each frame is first registered with a reference frame to compensate for its coordinates. Then, the objects in each frame are segmented through k-means clustering and morphological operations. Falsely detected objects are removed considering the actual size and the shape of the object. The centroid of the segmented area is considered the measured position for target tracking. The track is initialized with two-point differencing initialization, and the target states are continuously estimated by the interacting multiple model (IMM) filter. The nearest neighbor association rule assigns the measurement to the track. Tracks that move slower than the minimum speed are terminated at the proposed criteria. In the experiments, three videos were captured with a long-wave IR band thermal imaging camera mounted on a multirotor. In the first and second videos, eight pedestrians on a pavement and three hikers on a mountain on winter nights were captured, respectively. In the third video, two walking people with complex backgrounds were captured on a windy summer day. The image characteristics vary between videos depending on the climate and surrounding objects, but the proposed scheme shows the robust performance in all cases; the average root mean squared errors in position and velocity are obtained as 0.08 m and 0.53 m/s, respectively for the first video, 0.06 m and 0.58 m/s, respectively for the second video, and 0.18 m and 1.84 m/s, respectively for the third video. The proposed method reduces false tracks from 10 to 1 in the third video.

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Section: Introductionmentioning

confidence: 99%

“…A contour-based background-subtraction to extract foreground objects was presented [18]. A local adaptive thresholding method performs the pedestrian detection [19]. In [20], humans and animals were detected in difficult weather conditions using YOLO.…”

Section: Introductionmentioning

confidence: 99%

Moving People Tracking and False Track Removing with Infrared Thermal Imaging by a Multirotor

Yeom

2021

Drones

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“…Authors in [6] tracked pedestrians through infrared image fusion for the various visible spectrums. In 2015, Soundrapandiyan et al [7] tracked targets by adaptive thresholding and background subtracting of sample infrared images. Another research in 2015 by Rajkumar et al [8] was suggested the use of local thresholding for detecting of the pedestrians.…”

Section: Previous Methodsmentioning

confidence: 99%

“…Of course, someone might suppose it with the maximum spectral width up to 1000 nm [4,5]. Most of the thermal radiations from the objects at room temperature are almost infrared [6,7], and the largest source of this spectrum emission is the sun.…”

Section: Introductionmentioning

confidence: 99%

Detection and Tracking of Pedestrian Crosswalks Using Recurrent Neural Network Classifier and Histograms of Oriented Gradients in the Infrared Image Sequence

Shahrokhi¹,

Amini²

2020

IJETSI

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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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“…On other hand, some consider it to have a spectrum of up to 1000 nm [4,5]. Most of the heat radiation from objects at room temperature (the environment) is infrared [6,7]. Infrared radiation has various applications in various sciences, most notably infrared detectors, night vision devices, telescopes, food heaters, airplanes, and driving applications [8].…”

Section: Introductionmentioning

confidence: 99%

Supervised evolutionary learning: Use of gradient histogram & particle swarm algorithm to detection & tracking pedestrian in sequence of infrared images

2021

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Recently, tracking and pedestrian detection from various images have become one of the major issues in the field of image processing and statistical identification. In this regard, using evolutionary learning-based approaches to improve performance in different contexts can greatly influence the appropriate response. There are problems with pedestrian tracking/identification, such as low accuracy for detection, high processing time, and uncertainty in response to answers. Researchers are looking for new processing models that can accurately monitor one's position on the move. In this study, a hybrid algorithm for the automatic detection of pedestrian position is presented. It is worth noting that this method, contrary to the analysis of visible images, examines pedestrians' thermal and infrared components while walking and combines a neural network with maximum learning capability, wavelet kernel (Wavelet transform), and particle swarm optimization (PSO) to find parameters of learner model. Gradient histograms have a high effect on extracting features in infrared images. As well, the neural network algorithm can achieve its goal (pedestrian detection and tracking) by maximizing learning. The proposed method, despite the possibility of maximum learning, has a high speed in education, and results of various data sets in this field have been analyzed. The result indicates a negligible error in observing the infrared sequence of pedestrian movements, and it is suggested to use neural networks because of their precision and trying to boost the selection of their hyperparameters based on evolutionary algorithms.

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Adaptive Pedestrian Detection in Infrared Images Using Background Subtraction and Local Thresholding

Cited by 25 publications

References 25 publications

Moving People Tracking and False Track Removing with Infrared Thermal Imaging by a Multirotor

Moving People Tracking and False Track Removing with Infrared Thermal Imaging by a Multirotor

Detection and Tracking of Pedestrian Crosswalks Using Recurrent Neural Network Classifier and Histograms of Oriented Gradients in the Infrared Image Sequence

Supervised evolutionary learning: Use of gradient histogram & particle swarm algorithm to detection & tracking pedestrian in sequence of infrared images

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