Learning to Detect Event Sequences in Surveillance Streams at Very Low Frame Rate

2011 ◽  
pp. 117-144
Author(s):  
Paolo Lombardi ◽  
Cristina Versino
Keyword(s):  
Frame Rate ◽  
Event Sequences ◽  
Low Frame Rate

People Detection and Tracking in Low Frame-rate Dynamic Scenes

2016 ◽  
Vol 14 (4) ◽  
pp. 1966-1971 ◽  
Author(s):  
D.L. Siqueira ◽  
A. M. C. Machado
Keyword(s):  
Frame Rate ◽  
Dynamic Scenes ◽  
People Detection ◽  
Detection And Tracking ◽  
Low Frame Rate ◽  
Rate Dynamic

Multi-sperm tracking using Hungarian Kalman Filter on low frame rate video

2016 ◽  
Author(s):  
Grafika Jati ◽  
Alexander A S Gunawan ◽  
Silvia Werdhy Lestari ◽  
Wisnu Jatmiko ◽  
M H Hilman
Keyword(s):  
Kalman Filter ◽  
Frame Rate ◽  
Low Frame Rate

scholarly journals Estimating Body Related Soft Biometric Traits in Video Frames

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Olasimbo Ayodeji Arigbabu ◽  
Sharifah Mumtazah Syed Ahmad ◽  
Wan Azizun Wan Adnan ◽  
Salman Yussof ◽  
Vahab Iranmanesh ◽  
...  
Keyword(s):  
Human Subjects ◽  
Visual Surveillance ◽  
Frame Rate ◽  
Visual Appearance ◽  
Single View ◽  
Soft Biometrics ◽  
Related Information ◽  
Video Frames ◽  
Low Frame Rate ◽  
Biometric Information

Soft biometrics can be used as a prescreening filter, either by using single trait or by combining several traits to aid the performance of recognition systems in an unobtrusive way. In many practical visual surveillance scenarios, facial information becomes difficult to be effectively constructed due to several varying challenges. However, from distance the visual appearance of an object can be efficiently inferred, thereby providing the possibility of estimating body related information. This paper presents an approach for estimating body related soft biometrics; specifically we propose a new approach based on body measurement and artificial neural network for predicting body weight of subjects and incorporate the existing technique on single view metrology for height estimation in videos with low frame rate. Our evaluation on 1120 frame sets of 80 subjects from a newly compiled dataset shows that the mentioned soft biometric information of human subjects can be adequately predicted from set of frames.

Feasibility of pulse wave velocity estimation from low frame rate US sequences in vivo

2017 ◽  
Author(s):  
Maria Zontak ◽  
Matthew Bruce ◽  
Michelle Hippke ◽  
Alan Schwartz ◽  
Matthew O'Donnell
Keyword(s):  
Pulse Wave Velocity ◽  
Wave Velocity ◽  
Pulse Wave ◽  
Velocity Estimation ◽  
Frame Rate ◽  
Low Frame Rate

scholarly journals Multiple-Object-Tracking Algorithm Based on Dense Trajectory Voting in Aerial Videos

2019 ◽  
Vol 11 (19) ◽  
pp. 2278
Author(s):  
Tao Yang ◽  
Dongdong Li ◽  
Yi Bai ◽  
Fangbing Zhang ◽  
Sen Li ◽  
...  
Keyword(s):  
Optical Flow ◽  
Object Tracking ◽  
Target Tracking ◽  
Multiple Object Tracking ◽  
Tracking Algorithm ◽  
Frame Rate ◽  
Dense Optical Flow ◽  
Multiple Object ◽  
Dense Trajectory ◽  
Low Frame Rate

In recent years, UAV technology has developed rapidly. Due to the mobility, low cost, and variable monitoring altitude of UAVs, multiple-object detection and tracking in aerial videos has become a research hotspot in the field of computer vision. However, due to camera motion, small target size, target adhesion, and unpredictable target motion, it is still difficult to detect and track targets of interest in aerial videos, especially in the case of a low frame rate where the target position changes too much. In this paper, we propose a multiple-object-tracking algorithm based on dense-trajectory voting in aerial videos. The method models the multiple-target-tracking problem as a voting problem of the dense-optical-flow trajectory to the target ID, which can be applied to aerial-surveillance scenes and is robust to low-frame-rate videos. More specifically, we first built an aerial video dataset for vehicle targets, including a training dataset and a diverse test dataset. Based on this, we trained the neural network model by using a deep-learning method to detect vehicles in aerial videos. Thereafter, we calculated the dense optical flow in adjacent frames, and generated effective dense-optical-flow trajectories in each detection bounding box at the current time. When target IDs of optical-flow trajectories are known, the voting results of the optical-flow trajectories in each detection bounding box are counted. Finally, similarity between detection objects in adjacent frames was measured based on the voting results, and tracking results were obtained by data association. In order to evaluate the performance of this algorithm, we conducted experiments on self-built test datasets. A large number of experimental results showed that the proposed algorithm could obtain good target-tracking results in various complex scenarios, and performance was still robust at a low frame rate by changing the video frame rate. In addition, we carried out qualitative and quantitative comparison experiments between the algorithm and three state-of-the-art tracking algorithms, which further proved that this algorithm could not only obtain good tracking results in aerial videos with a normal frame rate, but also had excellent performance under low-frame-rate conditions.

Traffic Analytics With Low-Frame-Rate Videos

2018 ◽  
Vol 28 (4) ◽  
pp. 878-891 ◽  
Author(s):  
Zhiming Luo ◽  
Pierre-Marc Jodoin ◽  
Song-Zhi Su ◽  
Shao-Zi Li ◽  
Hugo Larochelle
Keyword(s):  
Frame Rate ◽  
Low Frame Rate
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