Pedestrian detection and tracking with night vision

By Fengliang Xu, Xia Liu, Kikuo Fujimura. IEEE transactions on intelligent transportation systems (Print), 2005

465 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/c3833f53c947bf89e2c06fd152ca4c7e5a651d6e

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Citations in the corpus

A Two-Stage Template Approach to Person Detection in Thermal Imagery (2005, background): “Furthermore, in collection 8, there were 2 people standing fairly still throughout the frames, and thus they were not often detected with the CSM screening template (lowering the Sensitivity value).”
Pedestrian Protection Systems: Issues, Survey, and Challenges (2007, methodology): “In thermal IR images, pedestrians that are warmer than the background form hot spots, which are used for detection, as in [102].”
Background-subtraction using contour-based fusion of thermal and visible imagery (2007, methodology): “A similar approach using Support Vector Machines is reported in [51].”
Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime (2018, background): “A lot of work for nighttime object detection/recognition has focused on human detection, by using FIR cameras [28], [8] or visible light cameras [15], or a combination of both [5].”
Long-Term Occupancy Analysis Using Graph-Based Optimisation in Thermal Imagery (2013, methodology): “The methods applied to thermal imaging span from simple thresholding and shape analysis [43, 17, 39, 15, 7] to more complex, but well-known methods such as HOG and SVM [42, 37, 41, 31, 26] as well as contour analysis [10, 9, 27, 38].”
Thermal cameras and applications: a survey (2013, methodology): “[193] proposed a system that uses SVM for detection and a combination of Kalman filter prediction and mean shift for tracking.”
Kernel-Based Object Tracking (2003, methodology): “Xu and Fujimura [77] used night vision for pedestrian detection and tracking, where the detection is performed by a support vector machine and the tracking is kernel-based.”
Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation (2019, background): “Several works pertain to human detection at nighttime, using FIR cameras [10, 37], visible light cameras [15], or a combination of both [4].”
Survey of Pedestrian Detection for Advanced Driver Assistance Systems (2010, result): “…SVM [56], Four Directional Features and Gaussian kernel SVM [59], and intensity image with Convolutional Neural Networks [85] or with an SVM [46], [73] Part-based approaches, contrary to the previous holistic techniques, combine the classification of different parts of the pedestrian body (e.g.,…”
Pedestrian Detection using Infrared images and Histograms of Oriented Gradients (2006, background): “Fig. 10 .Thisfigures showsanexample ofdisparity computation.”