A novel pedestrian detection and tracking with boosted HOG classifiers and Kalman filter

2016 ◽  
Author(s):  
Penny Chong ◽  
Yong Haur Tay
Keyword(s):  
Kalman Filter ◽  
Pedestrian Detection ◽  
Detection And Tracking

scholarly journals Detection and Tracking of Pedestrians Using Doppler LiDAR

2021 ◽  
Vol 13 (15) ◽  
pp. 2952
Author(s):  
Xiaoyi Peng ◽  
Jie Shan
Keyword(s):  
Kalman Filter ◽  
San Francisco ◽  
Autonomous Vehicles ◽  
Traffic Management ◽  
Pedestrian Detection ◽  
Supervised Machine Learning ◽  
Data Sets ◽  
Doppler Lidar ◽  
Tracking Accuracy ◽  
Detection And Tracking

Pedestrian detection and tracking is necessary for autonomous vehicles and traffic management. This paper presents a novel solution to pedestrian detection and tracking for urban scenarios based on Doppler LiDAR that records both the position and velocity of the targets. The workflow consists of two stages. In the detection stage, the input point cloud is first segmented to form clusters, frame by frame. A subsequent multiple pedestrian separation process is introduced to further segment pedestrians close to each other. While a simple speed classifier is capable of extracting most of the moving pedestrians, a supervised machine learning-based classifier is adopted to detect pedestrians with insignificant radial velocity. In the tracking stage, the pedestrian’s state is estimated by a Kalman filter, which uses the speed information to estimate the pedestrian’s dynamics. Based on the similarity between the predicted and detected states of pedestrians, a greedy algorithm is adopted to associate the trajectories with the detection results. The presented detection and tracking methods are tested on two data sets collected in San Francisco, California by a mobile Doppler LiDAR system. The results of the pedestrian detection demonstrate that the proposed two-step classifier can improve the detection performance, particularly for detecting pedestrians far from the sensor. For both data sets, the use of Doppler speed information improves the F1-score and the recall by 15% to 20%. The subsequent tracking from the Kalman filter can achieve 83.9–55.3% for the multiple object tracking accuracy (MOTA), where the contribution of the speed measurements is secondary and insignificant.

Pedestrian detection and tracking at crossroads

2004 ◽  
Author(s):  
Chia-Jung Pai ◽  
Hsiao-Rong Tyan ◽  
Yu-Ming Liang ◽  
Hong-Yuan Mark Liao ◽  
Sei-Wang Chen
Keyword(s):  
Pedestrian Detection ◽  
Detection And Tracking

scholarly journals MULTI OBJECT DETECTION AND TRACKING USING OPTICAL FLOW DENSITY – HUNGARIAN KALMAN FILTER (OFD - HKF) ALGORITHM FOR VEHICLE COUNTING

2018 ◽  
Vol 11 (1) ◽  
pp. 17 ◽  
Author(s):  
Muhamad Soleh ◽  
Grafika Jati ◽  
Muhammad Hafizhuddin Hilman
Keyword(s):  
Kalman Filter ◽  
Object Detection ◽  
Optical Flow ◽  
Motion Blur ◽  
Research Topic ◽  
Vehicle Tracking ◽  
Flow Density ◽  
Illumination Change ◽  
Dynamic Background ◽  
Detection And Tracking

Intelligent Transportation Systems (ITS) is one of the most developing research topic along with growing advance technology and digital information. The benefits of research topic on ITS are to address some problems related to traffic conditions. Vehicle detection and tracking is one of the main step to realize the benefits of ITS. There are several problems related to vehicles detection and tracking. The appearance of shadow, illumination change, challenging weather, motion blur and dynamic background such a big challenges issue in vehicles detection and tracking. Vehicles detection in this paper using the Optical Flow Density algorithm by utilizing the gradient of object displacement on video frames. Gradient image feature and HSV color space on Optical flow density guarantee the object detection in illumination change and challenging weather for more robust accuracy. Hungarian Kalman filter algorithm used for vehicle tracking. Vehicle tracking used to solve miss detection problems caused by motion blur and dynamic background. Hungarian kalman filter combine the recursive state estimation and optimal solution assignment. The future positon estimation makes the vehicles detected although miss detection occurance on vehicles. Vehicles counting used single line counting after the vehicles pass that line. The average accuracy for each process of vehicles detection, tracking, and counting were 93.6%, 88.2% and 88.2% respectively.

scholarly journals Convolutional Neural Network Using Kalman Filter for Human Detection and Tracking on RGB-D Video

2018 ◽  
Vol 12 (2) ◽  
pp. 105 ◽  
Author(s):  
Jovin Angelico ◽  
Ken Ratri Retno Wardani
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Convolutional Neural Network ◽  
Human Detection ◽  
Detection Accuracy ◽  
Tracking Accuracy ◽  
Lighting Condition ◽  
Detection And Tracking ◽  
Computation Efficiency ◽  
The Impact

The computer ability to detect human being by computer vision is still being improved both in accuracy or computation time. In low-lighting condition, the detection accuracy is usually low. This research uses additional information, besides RGB channels, namely a depth map that shows objects’ distance relative to the camera. This research integrates Cascade Classifier (CC) to localize the potential object, the Convolutional Neural Network (CNN) technique to identify the human and nonhuman image, and the Kalman filter technique to track human movement. For training and testing purposes, there are two kinds of RGB-D datasets used with different points of view and lighting conditions. Both datasets have been selected to remove images which contain a lot of noises and occlusions so that during the training process it will be more directed. Using these integrated techniques, detection and tracking accuracy reach 77.7%. The impact of using Kalman filter increases computation efficiency by 41%.

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