Visual and Thermal Data for Pedestrian and Cyclist Detection
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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Towards Autonomous Robotic Systems - 20th Annual Conference, TAROS 2019, Proceedings. ed. / Kaspar Althoefer; Jelizaveta Konstantinova; Ketao Zhang. United Kingdom: Springer, 2019. p. 223-234 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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TY - GEN
T1 - Visual and Thermal Data for Pedestrian and Cyclist Detection
AU - Ahmed, Sarfraz
AU - Huda, M. Nazmul
AU - Rajbhandari, Sujan
AU - Saha, Chitta
AU - Elshaw, Mark
AU - Kanarachos, Stratis
N1 - 20th Towards Autonomous Robotic Systems Conference, TAROS 2019 ; Conference date: 03-07-2019 Through 05-07-2019
PY - 2019
Y1 - 2019
N2 - With the continued advancement of autonomous vehicles and their implementation in public roads, accurate detection of vulnerable road users (VRUs) is vital for ensuring safety. To provide higher levels of safety for these VRUs, an effective detection system should be employed that can correctly identify VRUs in all types of environments (e.g. VRU appearance, crowded scenes) and conditions (e.g. fog, rain, night-time). This paper presents optimal methods of sensor fusion for pedestrian and cyclist detection using Deep Neural Networks (DNNs) for higher levels of feature abstraction. Typically, visible sensors have been utilized for this purpose. Recently, thermal sensors system or combination of visual and thermal sensors have been employed for pedestrian detection with advanced detection algorithm. DNNs have provided promising results for improving the accuracy of pedestrian and cyclist detection. This is because they are able to extract features at higher levels than typical hand-crafted detectors. Previous studies have shown that amongst the several sensor fusion techniques that exist, Halfway Fusion has provided the best results in terms of accuracy and robustness. Although sensor fusion and DNN implementation have been used for pedestrian detection, there is considerably less research undertaken for cyclist detection.
AB - With the continued advancement of autonomous vehicles and their implementation in public roads, accurate detection of vulnerable road users (VRUs) is vital for ensuring safety. To provide higher levels of safety for these VRUs, an effective detection system should be employed that can correctly identify VRUs in all types of environments (e.g. VRU appearance, crowded scenes) and conditions (e.g. fog, rain, night-time). This paper presents optimal methods of sensor fusion for pedestrian and cyclist detection using Deep Neural Networks (DNNs) for higher levels of feature abstraction. Typically, visible sensors have been utilized for this purpose. Recently, thermal sensors system or combination of visual and thermal sensors have been employed for pedestrian detection with advanced detection algorithm. DNNs have provided promising results for improving the accuracy of pedestrian and cyclist detection. This is because they are able to extract features at higher levels than typical hand-crafted detectors. Previous studies have shown that amongst the several sensor fusion techniques that exist, Halfway Fusion has provided the best results in terms of accuracy and robustness. Although sensor fusion and DNN implementation have been used for pedestrian detection, there is considerably less research undertaken for cyclist detection.
KW - Cyclist detection
KW - Deep Neural Networks
KW - Pedestrian detection
KW - Sensor fusion
U2 - 10.1007/978-3-030-25332-5_20
DO - 10.1007/978-3-030-25332-5_20
M3 - Conference contribution
SN - 978-3-030-25331-8
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 223
EP - 234
BT - Towards Autonomous Robotic Systems - 20th Annual Conference, TAROS 2019, Proceedings
A2 - Althoefer, Kaspar
A2 - Konstantinova, Jelizaveta
A2 - Zhang, Ketao
PB - Springer
CY - United Kingdom
ER -