Study and Analysis of Pedestrian Detection in Thermal Images Using YOLO and SVM

Narayanan, Ashwin; Kumar, Rajiv; Roselinkiruba, R.; Sharmila, T. Sree

doi:10.1109/wispnet51692.2021.9419443

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Veta et al [21] presented a technique for detecting objects at a distance by employing YOLO on lowquality thermal images. Another research [22] focused on pedestrian detection in thermal images using the histogram of gradient (HOG) and YOLO methods on FLIR [9] dataset and computed performance with a 70% accuracy on test data using the intersection over union technique. Further, Rumi et al [23] proposed a real-time human detection technique using YOLO-v3 on KAIST [10] thermal dataset, achieving 95.5% average precision on test data.…”

Section: A Related Literaturementioning

confidence: 99%

Evaluation of Thermal Imaging on Embedded GPU Platforms for Application in Vehicular Assistance Systems

Farooq

Shariff

Corcoran

2023

IEEE Trans. Intell. Veh.

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This study is focused on evaluating the real-time performance of thermal object detection for smart and safe vehicular systems by deploying the trained networks on GPU & single-board EDGE-GPU computing platforms for onboard automotive sensor suite testing. A novel large-scale C3I Thermal Automotive dataset comprising of > 35,000 distinct frames is acquired, processed, and open-sourced in challenging weather and environmental scenarios. The dataset is recorded from a lost-cost yet effective uncooled LWIR thermal camera, mounted standalone and on an electric vehicle to minimize mechanical vibrations. state-of-the-art YOLO-v5 networks variants are trained using four different public datasets as well newly acquired local dataset for optimal generalization of DNN by employing SGD optimizer. The effectiveness of trained networks is validated on extensive test data using various quantitative metrics which include precision, recall curve, mean average precision, and frames per second. The smaller network variant of YOLO is further optimized using TensorRT inference accelerator to explicitly boost the frames per second rate. Optimized network engine increases the frames per second rate by 3.5 times when testing on low power edge devices thus achieving 11 fps on Nvidia Jetson Nano and 60 fps on Nvidia Xavier NX development boards.

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Section: A Related Literaturementioning

confidence: 99%

Evaluation of Thermal Imaging on Embedded GPU Platforms for Application in Vehicular Assistance Systems

Farooq

Shariff

Corcoran

2023

IEEE Trans. Intell. Veh.

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“…For feature extraction, techniques such as Histogram of Oriented Gradients [93][94][95][96][97][98][99][100], Local Binary Pattern [101][102][103][104][105][106][107], Deformable Part Model [108][109][110][111][112][113], and Aggregate Channel Feature (ACF) [114][115][116][117][118] are included. On the other hand, methods such as Support Vector Machine (SVM) [94,105,[119][120][121][122], Decision Tree [123][124][125][126], Random Forest (RF) [127][128][129][130][131][132] and Ada-Boost [81,119,133,134] are used for the classification process.…”

Section: Object Detection and Classificationmentioning

confidence: 99%

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Reyes-Muñoz

Ibáñez

2022

Sensors

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There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.

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