scholarly journals SIMULTANEOUS DETECTION AND TRACKING OF PEDESTRIAN FROM PANORAMIC LASER SCANNING DATA

2016 ◽  
Vol III-3 ◽  
pp. 295-302
Author(s):  
Wen Xiao ◽  
Bruno Vallet ◽  
Konrad Schindler ◽  
Nicolas Paparoditis
Keyword(s):  
Energy Function ◽  
Laser Scanning ◽  
Moving Objects ◽  
Pedestrian Detection ◽  
Simultaneous Detection ◽  
Dynamic Environment ◽  
Space Time ◽  
Detection And Tracking ◽  
Nearest Point ◽  
Pedestrian Traffic

Pedestrian traffic flow estimation is essential for public place design and construction planning. Traditional data collection by human investigation is tedious, inefficient and expensive. Panoramic laser scanners, e.g. Velodyne HDL-64E, which scan surroundings repetitively at a high frequency, have been increasingly used for 3D object tracking. In this paper, a simultaneous detection and tracking (SDAT) method is proposed for precise and automatic pedestrian trajectory recovery. First, the dynamic environment is detected using two different methods, <i>Nearest-point</i> and <i>Max-distance</i>. Then, all the points on moving objects are transferred into a space-time (<i>x</i>, <i>y</i>, <i>t</i>) coordinate system. The pedestrian detection and tracking amounts to assign the points belonging to pedestrians into continuous trajectories in space-time. We formulate the point assignment task as an energy function which incorporates the point evidence, trajectory number, pedestrian shape and motion. A low energy trajectory will well explain the point observations, and have plausible trajectory trend and length. The method inherently filters out points from other moving objects and false detections. The energy function is solved by a two-step optimization process: tracklet detection in a short temporal window; and global tracklet association through the whole time span. Results demonstrate that the proposed method can automatically recover the pedestrians trajectories with accurate positions and low false detections and mismatches.

scholarly journals SIMULTANEOUS DETECTION AND TRACKING OF PEDESTRIAN FROM PANORAMIC LASER SCANNING DATA

2016 ◽  
Vol III-3 ◽  
pp. 295-302 ◽  
Author(s):  
Wen Xiao ◽  
Bruno Vallet ◽  
Konrad Schindler ◽  
Nicolas Paparoditis
Keyword(s):  
Energy Function ◽  
Laser Scanning ◽  
Moving Objects ◽  
Pedestrian Detection ◽  
Simultaneous Detection ◽  
Dynamic Environment ◽  
Space Time ◽  
Detection And Tracking ◽  
Nearest Point ◽  
Pedestrian Traffic

Pedestrian traffic flow estimation is essential for public place design and construction planning. Traditional data collection by human investigation is tedious, inefficient and expensive. Panoramic laser scanners, e.g. Velodyne HDL-64E, which scan surroundings repetitively at a high frequency, have been increasingly used for 3D object tracking. In this paper, a simultaneous detection and tracking (SDAT) method is proposed for precise and automatic pedestrian trajectory recovery. First, the dynamic environment is detected using two different methods, &lt;i&gt;Nearest-point&lt;/i&gt; and &lt;i&gt;Max-distance&lt;/i&gt;. Then, all the points on moving objects are transferred into a space-time (&lt;i&gt;x&lt;/i&gt;, &lt;i&gt;y&lt;/i&gt;, &lt;i&gt;t&lt;/i&gt;) coordinate system. The pedestrian detection and tracking amounts to assign the points belonging to pedestrians into continuous trajectories in space-time. We formulate the point assignment task as an energy function which incorporates the point evidence, trajectory number, pedestrian shape and motion. A low energy trajectory will well explain the point observations, and have plausible trajectory trend and length. The method inherently filters out points from other moving objects and false detections. The energy function is solved by a two-step optimization process: tracklet detection in a short temporal window; and global tracklet association through the whole time span. Results demonstrate that the proposed method can automatically recover the pedestrians trajectories with accurate positions and low false detections and mismatches.

scholarly journals A Survey of Low-Cost 3D Laser Scanning Technology

2021 ◽  
Vol 11 (9) ◽  
pp. 3938
Author(s):  
Shusheng Bi ◽  
Chang Yuan ◽  
Chang Liu ◽  
Jun Cheng ◽  
Wei Wang ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Moving Objects ◽  
Low Cost ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Future Research ◽  
3D Laser Scanning ◽  
Time Performance ◽  
Improving Accuracy ◽  
3D Lidar

By moving a commercial 2D LiDAR, 3D maps of the environment can be built, based on the data of a 2D LiDAR and its movements. Compared to a commercial 3D LiDAR, a moving 2D LiDAR is more economical. A series of problems need to be solved in order for a moving 2D LiDAR to perform better, among them, improving accuracy and real-time performance. In order to solve these problems, estimating the movements of a 2D LiDAR, and identifying and removing moving objects in the environment, are issues that should be studied. More specifically, calibrating the installation error between the 2D LiDAR and the moving unit, the movement estimation of the moving unit, and identifying moving objects at low scanning frequencies, are involved. As actual applications are mostly dynamic, and in these applications, a moving 2D LiDAR moves between multiple moving objects, we believe that, for a moving 2D LiDAR, how to accurately construct 3D maps in dynamic environments will be an important future research topic. Moreover, how to deal with moving objects in a dynamic environment via a moving 2D LiDAR has not been solved by previous research.

The Impact of the Detector on the Performances of a Multi Person Tracking System

2021 ◽  
Vol 15 ◽  
Author(s):  
Djalal Djarah ◽  
Abdallah Meraoumia ◽  
Mohamed Lakhdar Louazene
Keyword(s):  
System Performance ◽  
Detection System ◽  
Tracking System ◽  
Pedestrian Detection ◽  
Dynamic Environment ◽  
Human Gait ◽  
Detection And Tracking ◽  
Tracking By Detection ◽  
Multi Person Tracking ◽  
The Impact

Background: Pedestrian detection and tracking is an important area of study in real-world applications such as mobile robots, human-computer interaction, video surveillance, pedestrian protection systems, etc. As a result, it has attracted the interest of the scientific community. Objective: Certainly, tracking people is critical for numerous utility areas which cover unusual situations detection, like vicinity evaluation and sometimes change direction in human gait and partial occlusions. Researchers primary focus is to develop surveillance system that can work in a dynamic environment, but there are major issues and challenges involved in designing such systems. So, it has become a major issue and challenge to design a tracking system that can be more suitable for such situations. To this end, this paper presents a comparative evaluation of the tracking-by-detection system along with the publicly available pedestrian benchmark databases. Method: Unlike recent works where the person detection and tracking are usually treated separately, our work explores the joint use of the popular Simple Online and Real-time Tracking (SORT) method and the relevant visual detectors. Consequently, the choice of the detector is an important factor in the evaluation of the system performance. Results: Experimental results demonstrate that the performance of the tracking-by-detection system is closely related to the optimal selection of the detector and should be required prior to a rigorous evaluation. Conclusion: The study demonstrates how sensitive the system performance as a whole is to the challenging of the dataset. Furthermore, the efficiency of the detector and the detector-tracker combination are also depending on the dataset.

scholarly journals Real Time Pedestrian Detection and Tracking for Driver Assistance Systems

2019 ◽  
Vol 8 (5S3) ◽  
pp. 97-101
Keyword(s):  
Moving Objects ◽  
Pedestrian Detection ◽  
Autonomous Driving ◽  
Traffic Monitoring ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Shape Information ◽  
Detection And Tracking ◽  
Human Movements ◽  
Multiple Frames

In Autonomous driving technology detecting pedestrians and vehicles should be fast and efficient in order to avoid accidents. Pedestrian detection and tracking is challenging for complex real world scenes. In proposed system Kalman filter has been used to detect and track the pedestrians. From three frames initially eigen object is computed in video sequences for detection of moving objects, then shape information is used to classify humans and other objects. Moreover with the help of continues multiple frames occlusion between objects get calculated. In the proposed system an application is developed which gives automatic warning in case of doubtful activities performed by pedestrian of zone monitoring which can be used in various domains like defence and traffic monitoring. Proposed algorithm gives accurate moving object detection and advanced sensors are used to detect human movements ahead and alert the driver by using buzzer, result does not affect by body pose of individual

Multi Object Detection and Tracking from Video File

2014 ◽  
Vol 533 ◽  
pp. 218-225 ◽  
Author(s):  
Rapee Krerngkamjornkit ◽  
Milan Simic
Keyword(s):  
Computer Vision ◽  
Background Subtraction ◽  
Moving Objects ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Video Tracking ◽  
Partial Occlusion ◽  
Video File ◽  
Detection And Tracking ◽  
Over Time

This paper describes computer vision algorithms for detection, identification, and tracking of moving objects in a video file. The problem of multiple object tracking can be divided into two parts; detecting moving objects in each frame and associating the detections corresponding to the same object over time. The detection of moving objects uses a background subtraction algorithm based on Gaussian mixture models. The motion of each track is estimated by a Kalman filter. The video tracking algorithm was successfully tested using the BIWI walking pedestrians datasets [. The experimental results show that system can operate in real time and successfully detect, track and identify multiple targets in the presence of partial occlusion.

The Research of Moving Target Detection and Tracking System Based on DM6437

2015 ◽  
Vol 734 ◽  
pp. 203-206
Author(s):  
En Zeng Dong ◽  
Sheng Xu Yan ◽  
Kui Xiang Wei
Keyword(s):  
Target Detection ◽  
Digital Signal Processor ◽  
Moving Objects ◽  
Tracking System ◽  
Digital Signal ◽  
Gaussian Mixture ◽  
Background Model ◽  
Moving Target ◽  
Moving Target Detection ◽  
Detection And Tracking

In order to enhance the rapidity and the accuracy of moving target detection and tracking, and improve the speed of the algorithm on the DSP (digital signal processor), an active visual tracking system was designed based on the gaussian mixture background model and Meanshift algorithm on DM6437. The system use the VLIB library developed by TI, and through the method of gaussian mixture background model to detect the moving objects and use the Meanshift tracking algorithm based on color features to track the target in RGB space. Finally, the system is tested on the hardware platform, and the system is verified to be quickness and accuracy.

A New Approach to Phenotyping Disseminated Tumor Cells: Methodological advances and Clinical Implications

2000 ◽  
Vol 15 (1) ◽  
pp. 100-104 ◽  
Author(s):  
F. Noack ◽  
M. Schmitt ◽  
J. Bauer ◽  
D. Helmecke ◽  
W. Krüger ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Tumor Cells ◽  
Laser Scanning ◽  
Simultaneous Detection ◽  
Disseminated Tumor Cells ◽  
Laser Scanning Microscopy ◽  
Confocal Laser ◽  
Prognostic Impact ◽  
Primary Therapy ◽  
Double Immunofluorescence

At the time of primary therapy (surgery, systemic chemotherapy and/or radiation), disseminated tumor cells in the bone marrow can be found in almost one-third of patients with cancer of the breast, ovary, esophagus, stomach, colon, and other solid tumors. Whereas the prognostic impact of the mere presence of these cells is still a matter of debate, it has been shown that expression of tumor-associated antigens in disseminated tumor cells is linked to more aggressive disease. Therefore, further characterization of disseminated tumor cells at the protein and gene level has become increasingly important. To date, the most common detection method for disseminated tumor cells in the bone marrow is an immunocytochemical approach using cytokeratin-directed antibodies for detection of epithelial cells and the APAAP system for their visualization. We have established a new double immunofluorescence technique enabling simultaneous detection, phenotyping, and antigen quantification of disseminated tumor cells. Mononuclear cells from bone marrow are enriched by Ficoll gradient centrifugation and cytospins are prepared. Double immunofluorescence is performed using antibodies against cytokeratins 8/18/19 (mAb A45B/B3) and the uPA receptor CD87 (pAb HU277). CD87 expression is recorded by confocal laser scanning microscopy (CLSM) using fluorescence labeled latex beads as the reference; staining intensities of all the scans are then summed and quantified (extended focus). This protocol, originally designed for disseminated tumor cells in bone marrow, can also be applied to disseminated tumor cells in blood, to leukapheresis cells or to cells present in malignant ascites or other malignant effusions. The tumor cells detected may be used for gene and mRNA analyses. Furthermore, disseminated tumor cells also represent interesting targets for clinical studies on patient prognosis or prediction of therapy response as well as for specific tumor-biological therapies.

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