Evaluating Descriptors Performances for Object Tracking on Natural Video Data

2007 ◽  
pp. 352-363
Author(s):  
Mounia Mikram ◽  
Rémi Mégret ◽  
Yannick Berthoumieu
Keyword(s):  
Object Tracking ◽  
Video Data

scholarly journals Particle Filter with Gaussian Weighting for Vehicle Tracking

2011 ◽  
Vol 15 (6) ◽  
pp. 681-686
Author(s):  
Indah Agustien Siradjuddin ◽  
◽  
Muhammad Rahmat Widyanto ◽  
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Gaussian Distribution ◽  
Likelihood Function ◽  
Target Object ◽  
Video Data ◽  
Vehicle Tracking ◽  
Computational Time ◽  
Filter Method ◽  
Vehicle Motion

To track vehicle motion in data video, particle filter with Gaussian weighting is proposed. This method consists of four main stages. First, particles are generated to predict target’s location. Second, certain particles are searched and these particles are used to build Gaussian distribution. Third, weight of all particles is calculated based on Gaussian distribution. Fourth, particles are updated based on each weight. The proposed method could reduce computational time of tracking compared to that of conventional method of particle filter, since the proposed method does not have to calculate all particles weight using likelihood function. This method has been tested on video data with car as a target object. In average, this proposed method of particle filter is 60.61% times faster than particle filter method meanwhile the accuracy of tracking with this newmethod is comparable with particle filter method, which reach up to 86.87%. Hence this method is promising for real time object tracking application.

GEO-BASED AERIAL SURVEILLANCE VIDEO PROCESSING FOR SCENE UNDERSTANDING AND OBJECT TRACKING

2009 ◽  
Vol 23 (07) ◽  
pp. 1285-1307
Author(s):  
JIANGJIAN XIAO ◽  
HUI CHENG ◽  
FENG HAN ◽  
HARPREET SAWHNEY
Keyword(s):  
3D Reconstruction ◽  
Object Tracking ◽  
Video Processing ◽  
Moving Objects ◽  
Spatial Information ◽  
Ground Plane ◽  
Scene Understanding ◽  
Video Data ◽  
Surveillance Video ◽  
Aerial Surveillance

This paper presents an approach to extract semantic layers from aerial surveillance videos for scene understanding and object tracking. The input videos are captured by low flying aerial platforms and typically consist of strong parallax from non-ground-plane structures as well as moving objects. Our approach leverages the geo-registration between video frames and reference images (such as those available from Terraserver and Google satellite imagery) to establish a unique geo-spatial coordinate system for pixels in the video. The geo-registration process enables Euclidean 3D reconstruction with absolute scale unlike traditional monocular structure from motion where continuous scale estimation over long periods of time is an issue. Geo-registration also enables correlation of video data to other stored information sources such as GIS (Geo-spatial Information System) databases. In addition to the geo-registration and 3D reconstruction aspects, the other key contributions of this paper also include: (1) providing a reliable geo-based solution to estimate camera pose for 3D reconstruction, (2) exploiting appearance and 3D shape constraints derived from geo-registered videos for labeling of structures such as buildings, foliage, and roads for scene understanding, and (3) elimination of moving object detection and tracking errors using 3D parallax constraints and semantic labels derived from geo-registered videos. Experimental results on extended time aerial video data demonstrates the qualitative and quantitative aspects of our work.

scholarly journals A Novel Framework for Anomaly Detection in Video Surveillance using Convolutional LSTM

2020 ◽  
Vol 9 (4) ◽  
pp. 355-359
Keyword(s):  
Object Tracking ◽  
Video Surveillance ◽  
Event Detection ◽  
Video Data ◽  
Surveillance Systems ◽  
Abnormal Event Detection ◽  
Suggested Approach ◽  
Processing Technologies ◽  
Safety Requirements ◽  
Convolutional Lstm

Today, due to public safety requirements, surveillance systems have gained increased attention. Video data processing technologies such as the identification of activity [1], object tracking [2], crowd counting [3], and the detection of anomalies [ 4] have therefore been rapidly developing. In this study, we establish an unattended method for the detection of anomaly events in videos based on a ConvLSTM encoder-decoder to learn about the evolution of spatial characteristics. Our model only covers typical video events during preparation, whereas in testing the videos are both usual and abnormal. Experiments on the UCSD datasets confirm the validity of the suggested approach to abnormal event detection.

scholarly journals Channel Exchanging for RGB-T Tracking

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5800
Author(s):  
Long Zhao ◽  
Meng Zhu ◽  
Honge Ren ◽  
Lingjixuan Xue
Keyword(s):  
Object Tracking ◽  
Feature Fusion ◽  
Video Data ◽  
Visual Object ◽  
Complex Environments ◽  
The Poor ◽  
Fusion Methods ◽  
Core Elements ◽  
Benchmark Datasets ◽  
Single Modality

It is difficult to achieve all-weather visual object tracking in an open environment only utilizing single modality data input. Due to the complementarity of RGB and thermal infrared (TIR) data in various complex environments, a more robust object tracking framework can be obtained using video data of these two modalities. The fusion methods of RGB and TIR data are the core elements to determine the performance of the RGB-T object tracking method, and the existing RGB-T trackers have not solved this problem well. In order to solve the current low utilization of information intra single modality in aggregation-based methods and between two modalities in alignment-based methods, we used DiMP as the baseline tracker to design an RGB-T object tracking framework channel exchanging DiMP (CEDiMP) based on channel exchanging. CEDiMP achieves dynamic channel exchanging between sub-networks of different modes hardly adding any parameters during the feature fusion process. The expression ability of the deep features generated by our data fusion method based on channel exchanging is stronger. At the same time, in order to solve the poor generalization ability of the existing RGB-T object tracking methods and the poor ability in the long-term object tracking, more training of CEDiMP on the synthetic dataset LaSOT-RGBT is added. A large number of experiments demonstrate the effectiveness of the proposed model. CEDiMP achieves the best performance on two RGB-T object tracking benchmark datasets, GTOT and RGBT234, and performs outstandingly in the generalization testing.

A Fast-Iterative Data Association Technique for Multiple Object Tracking

2018 ◽  
Vol 12 (02) ◽  
pp. 261-285 ◽  
Author(s):  
Gurinderbeer Singh ◽  
Sreeraman Rajan ◽  
Shikharesh Majumdar
Keyword(s):  
Object Tracking ◽  
Data Association ◽  
Optimal Solution ◽  
Multiple Object Tracking ◽  
Optimization Techniques ◽  
Video Data ◽  
Tracking Accuracy ◽  
Computational Overhead ◽  
Multiple Object ◽  
Locally Optimal Solution

A massive amount of video data is recorded daily for forensic post analysis and computer vision applications. The analyses of this data often require multiple object tracking (MOT). Advancements in image analysis algorithms and global optimization techniques have improved the accuracy of MOT, often at the cost of slow processing speed which limits its applications only to small video datasets. With the focus on speed, a fast-iterative data association technique (FIDA) for MOT that uses a tracking-by-detection paradigm and finds a locally optimal solution with a low computational overhead is introduced. The performance analyses conducted on a set of benchmark video datasets show that the proposed technique is significantly faster (50–600 times) than the existing state-of-the-art techniques that produce a comparable tracking accuracy.

scholarly journals Efficient Reject Options for Particle Filter Object Tracking in Medical Applications

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2114
Author(s):  
Johannes Kummert ◽  
Alexander Schulz ◽  
Tim Redick ◽  
Nassim Ayoub ◽  
Ali Modabber ◽  
...  
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Particle Filtering ◽  
Threshold Value ◽  
Video Data ◽  
Threshold Strategy ◽  
Tracking Accuracy ◽  
Flexible Machine ◽  
Important Challenge ◽  
Classification Tasks

Reliable object tracking that is based on video data constitutes an important challenge in diverse areas, including, among others, assisted surgery. Particle filtering offers a state-of-the-art technology for this challenge. Becaise a particle filter is based on a probabilistic model, it provides explicit likelihood values; in theory, the question of whether an object is reliably tracked can be addressed based on these values, provided that the estimates are correct. In this contribution, we investigate the question of whether these likelihood values are suitable for deciding whether the tracked object has been lost. An immediate strategy uses a simple threshold value to reject settings with a likelihood that is too small. We show in an application from the medical domain—object tracking in assisted surgery in the domain of Robotic Osteotomies—that this simple threshold strategy does not provide a reliable reject option for object tracking, in particular if different settings are considered. However, it is possible to develop reliable and flexible machine learning models that predict a reject based on diverse quantities that are computed by the particle filter. Modeling the task in the form of a regression enables a flexible handling of different demands on the tracking accuracy; modeling the challenge as an ensemble of classification tasks yet surpasses the results, while offering the same flexibility.

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