17.1.3.2.5 Human Detection, People Detection, Pedestrians, Using Depth, Stereo

Fang, Z., López, A.M.,
Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation,
ITS(21), No. 11, November 2020, pp. 4773-4783.
IEEE DOI 2011
Task analysis, Pose estimation, Roads, Legged locomotion, Skeleton, Videos, Autonomous driving, ADAS, cyclists intention recognition BibRef

Yan, Y.[Yuyao], Xu, M.[Ming], Smith, J.S.[Jeremy S.], Shen, M.[Mo], Xi, J.[Jin],
Multicamera pedestrian detection using logic minimization,
PR(112), 2021, pp. 107703.
Elsevier DOI 2102
Pedestrian detection, Multicamera, Homography, Logic minimization, Video surveillance BibRef

Gao, X.[Xin], Xiong, Y.J.[Yi-Jin], Zhang, G.Y.[Guo-Ying], Deng, H.[Hui], Kou, K.K.[Kang-Kang],
Exploiting key points supervision and grouped feature fusion for multiview pedestrian detection,
PR(131), 2022, pp. 108866.
Elsevier DOI 2208
Multiview aggregation, Pedestrian detection, Key points, Grouped feature fusion BibRef

Katircioglu, I.[Isinsu], Rhodin, H.[Helge], Constantin, V.[Victor], Spörri, J.[Jörg], Salzmann, M.[Mathieu], Fua, P.[Pascal],
Self-Supervised Human Detection and Segmentation via Background Inpainting,
PAMI(44), No. 12, December 2022, pp. 9574-9588.
IEEE DOI 2212
BibRef
Earlier: A1, A2, A4, A5, A6, Only:
Human Detection and Segmentation via Multi-view Consensus,
ICCV21(2835-2844)
IEEE DOI 2203
Image segmentation, Training, Cameras, Optical imaging, Proposals, Optical sensors, Object detection, Self-supervised training, image inpainting. Location awareness, Geometry, Image segmentation, Motion segmentation, Training data, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Dai, X.B.[Xiao-Biao], Hu, J.P.[Jun-Ping], Luo, C.L.[Chun-Lei], Zerfa, H.[Houcine], Zhang, H.[Hai], Duan, Y.X.[Yu-Xia],
NIRPed: A Novel Benchmark for Nighttime Pedestrian and Its Distance Joint Detection,
ITS(24), No. 7, July 2023, pp. 6932-6942.
IEEE DOI 2307
Laser radar, Narrowband, Imaging, Cameras, Roads, Object detection, Lighting, Distance detection, faster-RCNN, pedestrian dataset BibRef

Qiu, R.[Rui], Xu, M.[Ming], Yan, Y.[Yuyao], Smith, J.S.[Jeremy S.], Ling, Y.C.[Yu-Chen],
PPM: A boolean optimizer for data association in multi-view pedestrian detection,
PR(156), 2024, pp. 110807.
Elsevier DOI 2408
Multi-camera, Data fusion, Video surveillance, Logic minimization, Pedestrian detection BibRef

Yamane, T.[Taiga], Suzuki, S.[Satoshi], Masumura, R.[Ryo], Tora, S.[Shotaro],
MVAFormer: RGB-Based Multi-View Spatio-Temporal Action Recognition with Transformer,
ICIP24(332-338)
IEEE DOI 2411
Image recognition, Stars, Transformers, Cameras, Vectors, Task analysis, Multi-view action recognition, transformer BibRef

Suzuki, S.[Satoshi], Tora, S.[Shotaro], Masumura, R.[Ryo],
Scene Generalized Multi-View Pedestrian Detection with Rotation-Based Augmentation and Regularization,
ICIP24(596-602)
IEEE DOI 2411
Training, Learning systems, Pedestrians, Layout, Feature extraction, Cameras, Data augmentation, Multi-view pedestrian detection, regularization BibRef

Ma, J.H.[Jia-Hao], Duan, Z.C.[Zi-Cheng], Zheng, L.[Liang], Nguyen, C.[Chuong],
Multiview Detection with Cardboard Human Modeling,
ACCV24(VI: 53-70).
Springer DOI 2412
Code:
WWW Link. Planar model from 1 view, integrate across views. BibRef

Blanch, M.R.[Miquel Romero], Li, Z.[Zenjie], Escalera, S.[Sergio], Nasrollahi, K.[Kamal],
LiDAR-Assisted 3D Human Detection for Video Surveillance,
RWSurvil24(123-131)
IEEE DOI Code:
WWW Link. 2404
Adaptation models, Solid modeling, Pedestrians, Laser radar, Merging, Focusing BibRef

Aung, S.[Sithu], Park, H.[Haesol], Jung, H.[Hyungjoo], Cho, J.[Junghyun],
Enhancing Multi-view Pedestrian Detection Through Generalized 3D Feature Pulling,
WACV24(1185-1194)
IEEE DOI 2404
Pedestrians, Computational modeling, Lighting, Feature extraction, Cameras, Algorithms, Image recognition and understanding, 3D computer vision BibRef

Hwang, J.[Jinwoo], Benz, P.[Philipp], Kim, P.[Pete],
Booster-SHOT: Boosting Stacked Homography Transformations for Multiview Pedestrian Detection with Attention,
WACV24(362-371)
IEEE DOI 2404
Measurement, Pedestrians, Computational modeling, Logic gates, Benchmark testing, Cameras, Algorithms, Image recognition and understanding BibRef

Pei, Y.F.[Yi-Fei], Shi, Z.P.[Zhi-Ping], Geng, Q.[Qichuan], Wang, Z.F.[Zhao-Fa], Zhang, Y.K.[Yong-Kang], Jiang, N.[Na],
RDEPD: Re-Exploring Depth Estimation for Pedestrian Detection,
ICIP23(2380-2384)
IEEE DOI 2312
BibRef

Lee, W.Y.[Wei-Yu], Jovanov, L.[Ljubomir], Philips, W.[Wilfried],
Cross-modality Attention and Multimodal Fusion Transformer for Pedestrian Detection,
RealWorld22(608-623).
Springer DOI 2304
BibRef

Lee, W.Y.[Wei-Yu], Jovanov, L.[Ljubomir], Philips, W.[Wilfried],
Multi-view Target Transformation for Pedestrian Detection,
RealWorld23(1-10)
IEEE DOI 2302
Learning systems, Limiting, Target recognition, Conferences, Network architecture, Distortion, Feature extraction BibRef

Vora, J.[Jeet], Dutta, S.[Swetanjal], Jain, K.[Kanishk], Karthik, S.[Shyamgopal], Gandhi, V.[Vineet],
Bringing Generalization to Deep Multi-View Pedestrian Detection,
RealWorld23(110-119)
IEEE DOI 2302
Training, Deep learning, Codes, Conferences, Benchmark testing BibRef

Qiu, R.[Rui], Xu, M.[Ming], Yan, Y.[Yuyao], Smith, J.S.[Jeremy S.], Yang, X.[Xi],
3D Random Occlusion and Multi-layer Projection for Deep Multi-camera Pedestrian Localization,
ECCV22(X:695-710).
Springer DOI 2211
BibRef

Song, L.C.[Liang-Chen], Wu, J.[Jialian], Yang, M.[Ming], Zhang, Q.[Qian], Li, Y.[Yuan], Yuan, J.S.[Jun-Song],
Stacked Homography Transformations for Multi-View Pedestrian Detection,
ICCV21(6029-6037)
IEEE DOI 2203
Benchmark testing, Cameras, Task analysis, Standards, Stereo, 3D from multiview and other sensors, Detection and localization in 2D and 3D BibRef

Li, T.H.[Tian-Hong], Fan, L.J.[Li-Jie], Yuan, Y.[Yuan], Katabi, D.[Dina],
Unsupervised Learning for Human Sensing Using Radio Signals,
WACV22(1091-1100)
IEEE DOI 2202
Radio frequency, Representation learning, RF signals, Supervised learning, Sensors, Trajectory, Transfer, Semi- and Un- supervised Learning BibRef

Lima, J.P.[João Paulo], Roberto, R.[Rafael], Figueiredo, L.[Lucas], Simões, F.[Francisco], Teichrieb, V.[Veronica],
Generalizable Multi-Camera 3D Pedestrian Detection,
LXCV21(1232-1240)
IEEE DOI 2109
Training, Sensitivity, Fuses, Pose estimation BibRef

Zhong, T.[Tao], Kim, W.[Wonjik], Tanaka, M.[Masayuki], Okutomi, M.[Masatoshi],
Human Segmentation with Dynamic LiDAR Data,
ICPR21(1166-1172)
IEEE DOI 2105
Image segmentation, Laser radar, Motion segmentation, Dynamics, Neural networks, Estimation BibRef

Zhao, Z., Zhang, J., Shan, S.,
Noise Robust Hard Example Mining for Human Detection with Efficient Depth-Thermal Fusion,
FG20(809-813)
IEEE DOI 2102
Noise robustness, Feature extraction, Training, Head, Thermal noise, Kernel, Detectors BibRef

Tupper, A., Green, R.,
Pedestrian Proximity Detection using RGB-D Data,
IVCNZ19(1-6)
IEEE DOI 2004
cameras, edge detection, image capture, image colour analysis, image filtering, image matching, image segmentation, Intel RealSense D435 stereo vision depth camera BibRef

Wang, F., Zhou, S., Panev, S., Han, J., Huang, D.,
Person-in-WiFi: Fine-Grained Person Perception Using WiFi,
ICCV19(5451-5460)
IEEE DOI 2004
image segmentation, learning (artificial intelligence), neural nets, pose estimation, receiving antennas, signal detection, Laser radar BibRef

Carletti, V.[Vincenzo], del Pizzo, L.[Luca], Percannella, G.[Gennaro], Vento, M.[Mario],
An efficient and effective method for people detection from top-view depth cameras,
AVSS18(1-6)
IEEE DOI 1806
BibRef
Earlier:
Benchmarking Two Algorithms for People Detection from Top-View Depth Cameras,
CIAP17(I:73-83).
Springer DOI 1711
cameras, computational complexity, image sensors, object detection, object tracking, optimisation, Magnetic heads BibRef

Lin, T., Tan, D.S., Tang, H., Chien, S., Chang, F., Chen, Y., Cheng, W., Hua, K.,
Pedestrian Detection from Lidar Data via Cooperative Deep and Hand-Crafted Features,
ICIP18(1922-1926)
IEEE DOI 1809
Feature extraction, Laser radar, Sensors, deep learning BibRef

Corbetta, A., Menkovski, V., Toschi, F.,
Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields,
AVSS17(1-6)
IEEE DOI 1806
learning (artificial intelligence), neural nets, object detection, object tracking, pedestrians, DL algorithms, Training data BibRef

Matti, D., Ekenel, H.K., Thiran, J.P.,
Combining LiDAR space clustering and convolutional neural networks for pedestrian detection,
AVSS17(1-6)
IEEE DOI 1806
feature extraction, image classification, neural nets, object detection, optical radar, pattern clustering, pedestrians, Visualization BibRef

Santoso, P.S., Hang, H.M.,
Learning-based human detection applied to RGB-D images,
ICIP17(3365-3369)
IEEE DOI 1803
Feature extraction, Principal component analysis, Proposals, Robustness, Sensors, Support vector machines, Training, CNNs, depth map BibRef

Li, H., Liu, J., Zhang, G., Gao, Y., Wu, Y.,
Multi-glimpse LSTM with color-depth feature fusion for human detection,
ICIP17(905-909)
IEEE DOI 1803
Context modeling, Feature extraction, Fuses, Image color analysis, Logic gates, Proposals, Training, Feature fusion, Human detection, RGB-D BibRef

Park, K., Kim, S., Sohn, K.,
Pedestrian proposal generation using depth-aware scale estimation,
ICIP17(2045-2049)
IEEE DOI 1803
Cameras, Estimation, Feature extraction, Image color analysis, Proposals, Radio frequency, Training, RGB-D object proposal, scale-invariant feature BibRef