Journal of Applied Optics, Volume. 45, Issue 3, 616(2024)
Low-light pedestrian detection and tracking algorithm based on autoencoder structure and improved Bytetrack
[1] Wenbin ZHU, Jing YUAN, Wenhao et al ZHU. Sequence-enhancement-based human detection and posture recognition of mobile robots in low illumination scenes. Robot, 44, 299-309(2022).
[2] Ziteng SHU, Zebin ZHANG, Yaozhe et al SONG. Low-light image object detection based on improved YOLOv5 algorithm. Laser & Optoelectronics Progress, 60, 77-84(2023).
[3] Y LUO, X CAO, J ZHANG et al. CE-FPN: enhancing channel information for object detection. Multimedia Tools and Applications, 81, 30685-30704(2022).
[4] K WANG, M Z LIU. Object recognition at night scene based on DCGAN and faster R-CNN. IEEE Access, 8, 193168-193182(2020).
[5] L ZHANG, G J QI, L WANG et al. AET vs. AED: unsupervised representation learning by auto-encoding transformations rather than data, 2547-2555(2019).
[6] Z GE, S LIU, F WANG et al. YOLOX: Exceeding YOLO series in 2021. http://arxiv.org/abs/2107.08430
[7] Z CUI, G J QI, L GU et al. Multitask aet with orthogonal tangent regularity for dark object detection, 2553-2562(2021).
[8] H C KARAIMER, M S BROWN. A software platform for manipulating the camera imaging pipeline, 873-888(2016).
[9] T BROOKS, B MILDENHALL, T XUE et al. Unprocessing images for learned raw denoising, 11036-11045(2019).
[11] Y ZHANG, P SUN, Y JIANG et al. Bytetrack: multi-object tracking by associating every detection box, 1-21(2022).
[12] K ZHOU, Y YANG, A CAVALLARO et al. Omni-scale feature learning for person re-identification, 3702-3712(2019).
[13] S HE, H LUO, P WANG et al. Transreid: transformer-based object re-identification, 15013-15022(2021).
[14] Y DU, J WAN, Y ZHAO et al. Giaotracker: a comprehensive framework for mcmot with global information and optimizing strategies in visdrone 2021, 2809-2819(2021).
[15] A BEWLEY, Z GE, L OTT et al. Simple online and realtime tracking, 3464-3468(2016).
[16] G MAGGIOLINO, A AHMAD, J CAO et al. Deep oc-sort: multi-pedestrian tracking by adaptive re-identification, 3025-3029(2023).
[18] P DENDORFER, A OSEP, A MILAN et al. Motchallenge: a benchmark for single-camera multiple target tracking. International Journal of Computer Vision, 129, 845-881(2021).
[19] C GUO, C LI, J GUO et al. Zero-reference deep curve estimation for low-light image enhancement, 1780-1789(2020).
[20] Y ZHANG, J ZHANG, X GUO. Kindling the darkness: a practical low-light image enhancer, 1632-1640(2019).
[21] W WU, J WENG, P ZHANG et al. Uretinex-net: Retinex-based deep unfolding network for low-light image enhancement, 5901-5910(2022).
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Zelin REN, Lan PANG, Chao WANG, Jiaheng LI, Fangyan ZHOU. Low-light pedestrian detection and tracking algorithm based on autoencoder structure and improved Bytetrack[J]. Journal of Applied Optics, 2024, 45(3): 616
Category: Research Articles
Received: Oct. 27, 2023
Accepted: --
Published Online: Jun. 2, 2024
The Author Email: ZHOU Fangyan (周方琰(1998—))