Automatic Dynamic Range Adjustment for Pedestrian Detection in Thermal (Infrared) Surveillance Videos

Oluyide, Oluwakorede Monica; Tapamo, Jules-Raymond; Walingo, Tom

doi:10.3390/s22051728

Cited by 5 publications

(3 citation statements)

References 37 publications

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“…The proposed method is tested on the following public databases as previously described in Oluyide, Tapamo & Walingo (2022) : The Linkoping Thermal InfraRed (LTIR) dataset put forward by Berg, Ahlberg & Felsberg (2015) LITIV dataset put forward by Torabi, Massé & Bilodeau (2012) OTCBVS benchmark – Terravic Motion IR database put forward by Miezianko (2005) OTCBVS benchmark – Ohio State University (OSU) thermal pedestrian database put forward by Davis & Keck (2005) …”

Section: Resultsmentioning

confidence: 99%

A semi-automatic motion-constrained Graph Cut algorithm for Pedestrian Detection in thermal surveillance videos

Oluyide¹,

Tapamo²,

Walingo³

2022

PeerJ Computer Science

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This article presents a semi-automatic algorithm that can detect pedestrians from the background in thermal infrared images. The proposed method is based on the powerful Graph Cut optimisation algorithm which produces exact solutions for binary labelling problems. An additional term is incorporated into the energy formulation to bias the detection framework towards pedestrians. Therefore, the proposed method obtains reliable and robust results through user-selected seeds and the inclusion of motion constraints. An additional advantage is that it enables the algorithm to generalise well across different databases. The effectiveness of our method is demonstrated on four public databases and compared with several methods proposed in the literature and the state-of-the-art. The method obtained an average precision of 98.92% and an average recall of 99.25% across the four databases considered and outperformed methods which made use of the same databases.

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Section: Resultsmentioning

confidence: 99%

A semi-automatic motion-constrained Graph Cut algorithm for Pedestrian Detection in thermal surveillance videos

Oluyide¹,

Tapamo²,

Walingo³

2022

PeerJ Computer Science

Self Cite

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“…Biswas et al [36] used local steering kernel (LSK) as low-level feature descriptors for pedestrian detection in far infrared images. Oluyide et al [37] proposed an approach for candidate generation and ROI extraction using histogram specification and partitioning for pedestrian detection in IR surveillance videos.…”

Section: Related Workmentioning

confidence: 99%

Human Detection in Aerial Thermal Images Using Faster R-CNN and SSD Algorithms

et al. 2022

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The automatic detection of humans in aerial thermal imagery plays a significant role in various real-time applications, such as surveillance, search and rescue and border monitoring. Small target size, low resolution, occlusion, pose, and scale variations are the significant challenges in aerial thermal images that cause poor performance for various state-of-the-art object detection algorithms. Though many deep-learning-based object detection algorithms have shown impressive performance for generic object detection tasks, their ability to detect smaller objects in the aerial thermal images is analyzed through this study. This work carried out the performance evaluation of Faster R-CNN and single-shot multi-box detector (SSD) algorithms with different backbone networks to detect human targets in aerial view thermal images. For this purpose, two standard aerial thermal datasets having human objects of varying scale are considered with different backbone networks, such as ResNet50, Inception-v2, and MobileNet-v1. The evaluation results demonstrate that the Faster R-CNN model trained with the ResNet50 network architecture out-performed in terms of detection accuracy, with a mean average precision (mAP at 0.5 IoU) of 100% and 55.7% for the test data of the OSU thermal dataset and AAU PD T datasets, respectively. SSD with MobileNet-v1 achieved the highest detection speed of 44 frames per second (FPS) on the NVIDIA GeForce GTX 1080 GPU. Fine-tuning the anchor parameters of the Faster R-CNN ResNet50 and SSD Inception-v2 algorithms caused remarkable improvement in mAP by 10% and 3.5%, respectively, for the challenging AAU PD T dataset. The experimental results demonstrated the application of Faster R-CNN and SSD algorithms for human detection in aerial view thermal images, and the impact of varying backbone network and anchor parameters on the performance improvement of these algorithms.

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“…Firstly, the pedestrian features (e.g., color, edge, texture, etc.) are extracted by handcraft; then, the classifier is trained with these features; finally, sliding windows are utilized for object localization [5]. Compared with traditional detection methods, ConvNets can implement end-to-end pedestrian detection, which is independent of expert experience [6].…”

Section: Introductionmentioning

confidence: 99%

A Thermal Infrared Pedestrian-Detection Method for Edge Computing Devices

You

Liu

et al. 2022

Sensors

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The thermal imaging pedestrian-detection system has excellent performance in different lighting scenarios, but there are problems regarding weak texture, object occlusion, and small objects. Meanwhile, large high-performance models have higher latency on edge devices with limited computing power. To solve the above problems, in this paper, we propose a real-time thermal imaging pedestrian-detection method for edge computing devices. Firstly, we utilize multi-scale mosaic data augmentation to enhance the diversity and texture of objects, which alleviates the impact of complex environments. Then, the parameter-free attention mechanism is introduced into the network to enhance features, which barely increases the computing cost of the network. Finally, we accelerate multi-channel video detection through quantization and multi-threading techniques on edge computing devices. Additionally, we create a high-quality thermal infrared dataset to facilitate the research. The comparative experiments on the self-built dataset, YDTIP, and three public datasets, with other methods show that our method also has certain advantages.

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Automatic Dynamic Range Adjustment for Pedestrian Detection in Thermal (Infrared) Surveillance Videos

Cited by 5 publications

References 37 publications

A semi-automatic motion-constrained Graph Cut algorithm for Pedestrian Detection in thermal surveillance videos

A semi-automatic motion-constrained Graph Cut algorithm for Pedestrian Detection in thermal surveillance videos

Human Detection in Aerial Thermal Images Using Faster R-CNN and SSD Algorithms

A Thermal Infrared Pedestrian-Detection Method for Edge Computing Devices

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