scholarly journals Object tracking using motion flow projection for pan-tilt configuration

2020 ◽  
Vol 10 (5) ◽  
pp. 4687
Author(s):  
Luma Issa Abdul-Kreem ◽  
Hussam K. Abdul-Ameer
Keyword(s):  
Object Tracking ◽  
Optic Flow ◽  
Degrees Of Freedom ◽  
Region Of Interest ◽  
Target Object ◽  
Rigid Transformation ◽  
Camera View ◽  
Tracking Model ◽  
Flow Vectors ◽  
Motion Flow

We propose a new object tracking model for two degrees of freedom mechanism. Our model uses a reverse projection from a camera plane to a world plane. Here, the model takes advantage of optic flow technique by re-projecting the flow vectors from the image space into world space. A pan-tilt (PT) mounting system is used to verify the performance of our model and maintain the tracked object within a region of interest (ROI). This system contains two servo motors to enable a webcam rotating along PT axes. The PT rotation angles are estimated based on a rigid transformation of the the optic flow vectors in which an idealized translation matrix followed by two rotational matrices around PT axes are used. Our model was tested and evaluated using different objects with different motions. The results reveal that our model can keep the target object within a certain region in the camera view.

scholarly journals Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2841
Author(s):  
Khizer Mehmood ◽  
Abdul Jalil ◽  
Ahmad Ali ◽  
Baber Khan ◽  
Maria Murad ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Environmental Changes ◽  
Correlation Filter ◽  
Temporal Context ◽  
Visual Object ◽  
Target Model ◽  
Change Of Scale ◽  
Tracking Model ◽  
Spatio Temporal

Despite eminent progress in recent years, various challenges associated with object tracking algorithms such as scale variations, partial or full occlusions, background clutters, illumination variations are still required to be resolved with improved estimation for real-time applications. This paper proposes a robust and fast algorithm for object tracking based on spatio-temporal context (STC). A pyramid representation-based scale correlation filter is incorporated to overcome the STC’s inability on the rapid change of scale of target. It learns appearance induced by variations in the target scale sampled at a different set of scales. During occlusion, most correlation filter trackers start drifting due to the wrong update of samples. To prevent the target model from drift, an occlusion detection and handling mechanism are incorporated. Occlusion is detected from the peak correlation score of the response map. It continuously predicts target location during occlusion and passes it to the STC tracking model. After the successful detection of occlusion, an extended Kalman filter is used for occlusion handling. This decreases the chance of tracking failure as the Kalman filter continuously updates itself and the tracking model. Further improvement to the model is provided by fusion with average peak to correlation energy (APCE) criteria, which automatically update the target model to deal with environmental changes. Extensive calculations on the benchmark datasets indicate the efficacy of the proposed tracking method with state of the art in terms of performance analysis.

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.

scholarly journals A System for Movement Detecting Congestion.

2014 ◽  
Vol 13 (3) ◽  
pp. 4302-4307
Author(s):  
Reeja S. R ◽  
Dr. N. P. Kavya
Keyword(s):  
Optical Flow ◽  
Optic Flow ◽  
Video Segmentation ◽  
Video Sequences ◽  
Protective Measures ◽  
Flow Computation ◽  
Congested Area ◽  
Crowd Motion ◽  
Flow Vectors ◽  
Early Information

In this paper, we present a system for tracking and provide early information of hazardous locationsin huge gatherings. It is based on optic flow estimations and detects sequences of crowd motion that are characteristic for devastating congestions. For optic flow computation, Lucas- Kanade method is employed to determine the optical flow vectors for the gathered video. Segmentation of video sequences is done and optic flow is determined for respective segments. A threshold optic flow is chosen in such a way that the tracking of congested area in video is easilydoneby comparing it with respective segment’s determined optic flow values. Finally, we present the location of crowd congestion which helps in taking further protective measures to handle unusual events.  

Auto-attentional mechanism in multi-domain convolutional neural networks for improving object tracking

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jinchao Huang
Keyword(s):  
Object Tracking ◽  
Moving Objects ◽  
Motion Blur ◽  
Feature Maps ◽  
Content Type ◽  
Objects Tracking ◽  
Tracking Model ◽  
Background Motion ◽  
Gated Recurrent Unit ◽  
Attentional Mechanism

PurposeMulti-domain convolutional neural network (MDCNN) model has been widely used in object recognition and tracking in the field of computer vision. However, if the objects to be tracked move rapid or the appearances of moving objects vary dramatically, the conventional MDCNN model will suffer from the model drift problem. To solve such problem in tracking rapid objects under limiting environment for MDCNN model, this paper proposed an auto-attentional mechanism-based MDCNN (AA-MDCNN) model for the rapid moving and changing objects tracking under limiting environment.Design/methodology/approachFirst, to distinguish the foreground object between background and other similar objects, the auto-attentional mechanism is used to selectively aggregate the weighted summation of all feature maps to make the similar features related to each other. Then, the bidirectional gated recurrent unit (Bi-GRU) architecture is used to integrate all the feature maps to selectively emphasize the importance of the correlated feature maps. Finally, the final feature map is obtained by fusion the above two feature maps for object tracking. In addition, a composite loss function is constructed to solve the similar but different attribute sequences tracking using conventional MDCNN model.FindingsIn order to validate the effectiveness and feasibility of the proposed AA-MDCNN model, this paper used ImageNet-Vid dataset to train the object tracking model, and the OTB-50 dataset is used to validate the AA-MDCNN tracking model. Experimental results have shown that the augmentation of auto-attentional mechanism will improve the accuracy rate 2.75% and success rate 2.41%, respectively. In addition, the authors also selected six complex tracking scenarios in OTB-50 dataset; over eleven attributes have been validated that the proposed AA-MDCNN model outperformed than the comparative models over nine attributes. In addition, except for the scenario of multi-objects moving with each other, the proposed AA-MDCNN model solved the majority rapid moving objects tracking scenarios and outperformed than the comparative models on such complex scenarios.Originality/valueThis paper introduced the auto-attentional mechanism into MDCNN model and adopted Bi-GRU architecture to extract key features. By using the proposed AA-MDCNN model, rapid object tracking under complex background, motion blur and occlusion objects has better effect, and such model is expected to be further applied to the rapid object tracking in the real world.

Complementary Object Tracking Using Average Peak-to-Correlation Energy

2021 ◽  
Author(s):  
Kosuke Honda ◽  
Hamido Fujita
Keyword(s):  
Neural Networks ◽  
Object Tracking ◽  
Convolutional Neural Networks ◽  
Correlation Energy ◽  
Target Object ◽  
The Other ◽  
Tracking Performance ◽  
Correlation Filter ◽  
Evaluation Index ◽  
Siamese Network

In recent years, template-based methods such as Siamese network trackers and Correlation Filter (CF) based trackers have achieved state-of-the-art performance in several benchmarks. Recent Siamese network trackers use deep features extracted from convolutional neural networks to locate the target. However, the tracking performance of these trackers decreases when there are similar distractors to the object and the target object is deformed. On the other hand, correlation filter (CF)-based trackers that use handcrafted features (e.g., HOG features) to spatially locate the target. These two approaches have complementary characteristics due to differences in learning methods, features used, and the size of search regions. Also, we found that these trackers are complementary in terms of performance in benchmarking. Therefore, we propose the “Complementary Tracking framework using Average peak-to-correlation energy” (CTA). CTA is the generic object tracking framework that connects CF-trackers and Siamese-trackers in parallel and exploits the complementary features of these. In CTA, when a tracking failure of the Siamese tracker is detected using Average peak-to-correlation energy (APCE), which is an evaluation index of the response map matrix, the CF-trackers correct the output. In experimental on OTB100, CTA significantly improves the performance over the original tracker for several combinations of Siamese-trackers and CF-rackers.

A Neural Network for the Processing of Optic Flow from Ego-Motion in Man and Higher Mammals

1993 ◽  
Vol 5 (3) ◽  
pp. 374-391 ◽  
Author(s):  
Markus Lappe ◽  
Josef P. Rauschecker
Keyword(s):  
Neural Network ◽  
Motion Perception ◽  
Network Model ◽  
Neural Network Model ◽  
Optic Flow ◽  
Cortical Area ◽  
Visual Motion ◽  
Flow Fields ◽  
Subspace Algorithm ◽  
Motion Flow

Interest in the processing of optic flow has increased recently in both the neurophysiological and the psychophysical communities. We have designed a neural network model of the visual motion pathway in higher mammals that detects the direction of heading from optic flow. The model is a neural implementation of the subspace algorithm introduced by Heeger and Jepson (1990). We have tested the network in simulations that are closely related to psychophysical and neurophysiological experiments and show that our results are consistent with recent data from both fields. The network reproduces some key properties of human ego-motion perception. At the same time, it produces neurons that are selective for different components of ego-motion flow fields, such as expansions and rotations. These properties are reminiscent of a subclass of neurons in cortical area MSTd, the triple-component neurons. We propose that the output of such neurons could be used to generate a computational map of heading directions in or beyond MST.

Object Tracking Using Probabilistic Principal Component Analysis Based on Particle Filtering Framework

2011 ◽  
Vol 341-342 ◽  
pp. 790-797 ◽  
Author(s):  
Zhi Yan Xiang ◽  
Tie Yong Cao ◽  
Peng Zhang ◽  
Tao Zhu ◽  
Jing Feng Pan
Keyword(s):  
Principal Component Analysis ◽  
Object Tracking ◽  
Particle Filtering ◽  
Principal Component ◽  
Component Analysis ◽  
Dimensional Subspace ◽  
Target Object ◽  
Scale Invariant ◽  
Low Dimensional ◽  
Probabilistic Principal Component Analysis

In this paper, an object tracking approach is introduced for color video sequences. The approach presents the integration of color distributions and probabilistic principal component analysis (PPCA) into particle filtering framework. Color distributions are robust to partial occlusion, are rotation and scale invariant and are calculated efficiently. Principal Component Analysis (PCA) is used to update the eigenbasis and the mean, which can reflect the appearance changes of the tracked object. And a low dimensional subspace representation of PPCA efficiently adapts to these changes of appearance of the target object. At the same time, a forgetting factor is incorporated into the updating process, which can be used to economize on processing time and enhance the efficiency of object tracking. Computer simulation experiments demonstrate the effectiveness and the robustness of the proposed tracking algorithm when the target object undergoes pose and scale changes, defilade and complex background.

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