Crowded scene analysis is currently a hot and challenging topic in computer vision field. The ability to analyze motion patterns from videos is a difficult, but critical part of this problem. In this paper, we propose...Crowded scene analysis is currently a hot and challenging topic in computer vision field. The ability to analyze motion patterns from videos is a difficult, but critical part of this problem. In this paper, we propose a novel approach for the analysis of motion patterns by clustering the tracklets using an unsupervised hierarchical clustering algorithm, where the similarity between tracklets is measured by the Longest Common Subsequences. The tracklets are obtained by tracking dense points under three effective rules, therefore enabling it to capture the motion patterns in crowded scenes. The analysis of motion patterns is implemented in a completely unsupervised way, and the tracklets are clustered automatically through hierarchical clustering algorithm based on a graphic model. To validate the performance of our approach, we conducted experimental evaluations on two datasets. The results reveal the precise distributions of motion patterns in current crowded videos and demonstrate the effectiveness of our approach.展开更多
Multi-object tracking is a vital problem as many applications require better tracking approaches.Although learning-based detectors are becoming extremely powerful,there are few tracking methods designed to work with t...Multi-object tracking is a vital problem as many applications require better tracking approaches.Although learning-based detectors are becoming extremely powerful,there are few tracking methods designed to work with them in real time.We explored an efficient flexible online vehicle tracking-by-detection framework suitable for real-virtual mapping systems,which combines a non-recursive temporal window search with delayed output and produces stable trajectories despite noisy detection responses.Its computation speed meets the real-time requirements,whereas its performance is comparable with that of state-of-the-art online trackers on the DETRAC dataset.The trajectories from our approach also contain the target class and color information important for virtual vehicle motion reconstruction.展开更多
An initial orbit determination (IOD) solution from angles-only observations of a single short orbit arc is often required for applications such as tracklet association and fast reacquisition of a newly detected space ...An initial orbit determination (IOD) solution from angles-only observations of a single short orbit arc is often required for applications such as tracklet association and fast reacquisition of a newly detected space object. Modern optical observations can collect tens or even hundreds of data points over a short arc, thus enabling a large number of IOD solutions to be determined when using an IOD algorithm of 3 lines of sight (3-LOSs), such as the Gooding algorithm. It is necessary but difficult to find an optimal solution from a solution pool, particularly in the case of too short arc (TSA). Another issue in using 3-LOSs IOD methods is the neglect of perturbation effects on the observations. That is, 3-LOSs IOD methods are developed in the 2-body frame, but the observations are perturbed. Thus, the IOD solutions may have additional errors if the observations are not corrected for perturbation effects. In this study, we investigate the distribution of the semi-major axis and eccentricity of IOD solutions in a pool and find that choosing the solution with the maximum kernel density in the distribution is a much better way to determine the final solution from the pool. We also propose a technique to correct J2 secular effects on observed angle data. We use the Gooding algorithm as the basic 3-LOSs IOD algorithm to demonstrate the effectiveness of the proposed techniques in improving the IOD accuracy in the cases of short-arc ground-based observations and space-based simulation data.展开更多
In this study,a novel approach based on the U-Net deep neural network for image segmentation is leveraged for real-time extraction of tracklets from optical acquisitions.As in all machine learning(ML)applications,a se...In this study,a novel approach based on the U-Net deep neural network for image segmentation is leveraged for real-time extraction of tracklets from optical acquisitions.As in all machine learning(ML)applications,a series of steps is required for a working pipeline:dataset creation,preprocessing,training,testing,and post-processing to refine the trained network output.Online websites usually lack ready-to-use datasets;thus,an in-house application artificially generates 360 labeled images.Particularly,this software tool produces synthetic night-sky shots of transiting objects over a specified location and the corresponding labels:dual-tone pictures with black backgrounds and white tracklets.Second,both images and labels are downscaled in resolution and normalized to accelerate the training phase.To assess the network performance,a set of both synthetic and real images was inputted.After the preprocessing phase,real images were fine-tuned for vignette reduction and background brightness uniformity.Additionally,they are down-converted to eight bits.Once the network outputs labels,post-processing identifies the centroid right ascension and declination of the object.The average processing time per real image is less than 1.2 s;bright tracklets are easily detected with a mean centroid angular error of 0.25 deg in 75%of test cases with a 2 deg field-of-view telescope.These results prove that an ML-based method can be considered a valid choice when dealing with trail reconstruction,leading to acceptable accuracy for a fast image processing pipeline.展开更多
Real driving scenarios,due to occlusions and disturbances,provide disordered and noisy measurements,which makes the task of multi-object tracking quite challenging.Conventional approach is to find deterministic data a...Real driving scenarios,due to occlusions and disturbances,provide disordered and noisy measurements,which makes the task of multi-object tracking quite challenging.Conventional approach is to find deterministic data association;however,it has unstable performance in high clutter density.This paper proposes a novel probabilistic tracklet-enhanced multiple object tracker(PTMOT),which integrates Poisson multi-Bernoulli mixture(PMBM)filter with confidence of tracklets.The proposed method is able to realize efficient and robust probabilistic association for 3D multi-object tracking(MOT)and improve the PMBM filter’s continuity by smoothing single target hypothesis with global hypothesis.It consists of two key parts.First,the PMBM tracker based on sets of tracklets is implemented to realize probabilistic fusion of disordered measure-ments.Second,the confidence of tracklets is smoothed through a smoothing-while-filtering approach.Extensive MOT tests on nuScenes tracking dataset demonstrate that the proposed method achieves superior performance in different modalities.展开更多
基金supported in part by National Basic Research Program of China (973 Program) under Grant No. 2011CB302203the National Natural Science Foundation of China under Grant No. 61273285
文摘Crowded scene analysis is currently a hot and challenging topic in computer vision field. The ability to analyze motion patterns from videos is a difficult, but critical part of this problem. In this paper, we propose a novel approach for the analysis of motion patterns by clustering the tracklets using an unsupervised hierarchical clustering algorithm, where the similarity between tracklets is measured by the Longest Common Subsequences. The tracklets are obtained by tracking dense points under three effective rules, therefore enabling it to capture the motion patterns in crowded scenes. The analysis of motion patterns is implemented in a completely unsupervised way, and the tracklets are clustered automatically through hierarchical clustering algorithm based on a graphic model. To validate the performance of our approach, we conducted experimental evaluations on two datasets. The results reveal the precise distributions of motion patterns in current crowded videos and demonstrate the effectiveness of our approach.
文摘Multi-object tracking is a vital problem as many applications require better tracking approaches.Although learning-based detectors are becoming extremely powerful,there are few tracking methods designed to work with them in real time.We explored an efficient flexible online vehicle tracking-by-detection framework suitable for real-virtual mapping systems,which combines a non-recursive temporal window search with delayed output and produces stable trajectories despite noisy detection responses.Its computation speed meets the real-time requirements,whereas its performance is comparable with that of state-of-the-art online trackers on the DETRAC dataset.The trajectories from our approach also contain the target class and color information important for virtual vehicle motion reconstruction.
基金supported by the National Natural Science Foundation of China(grant nos.12103035 and 12373083)the Fundamental Research Funds for the Central Universities(grant no.2042023gf0007).
文摘An initial orbit determination (IOD) solution from angles-only observations of a single short orbit arc is often required for applications such as tracklet association and fast reacquisition of a newly detected space object. Modern optical observations can collect tens or even hundreds of data points over a short arc, thus enabling a large number of IOD solutions to be determined when using an IOD algorithm of 3 lines of sight (3-LOSs), such as the Gooding algorithm. It is necessary but difficult to find an optimal solution from a solution pool, particularly in the case of too short arc (TSA). Another issue in using 3-LOSs IOD methods is the neglect of perturbation effects on the observations. That is, 3-LOSs IOD methods are developed in the 2-body frame, but the observations are perturbed. Thus, the IOD solutions may have additional errors if the observations are not corrected for perturbation effects. In this study, we investigate the distribution of the semi-major axis and eccentricity of IOD solutions in a pool and find that choosing the solution with the maximum kernel density in the distribution is a much better way to determine the final solution from the pool. We also propose a technique to correct J2 secular effects on observed angle data. We use the Gooding algorithm as the basic 3-LOSs IOD algorithm to demonstrate the effectiveness of the proposed techniques in improving the IOD accuracy in the cases of short-arc ground-based observations and space-based simulation data.
文摘In this study,a novel approach based on the U-Net deep neural network for image segmentation is leveraged for real-time extraction of tracklets from optical acquisitions.As in all machine learning(ML)applications,a series of steps is required for a working pipeline:dataset creation,preprocessing,training,testing,and post-processing to refine the trained network output.Online websites usually lack ready-to-use datasets;thus,an in-house application artificially generates 360 labeled images.Particularly,this software tool produces synthetic night-sky shots of transiting objects over a specified location and the corresponding labels:dual-tone pictures with black backgrounds and white tracklets.Second,both images and labels are downscaled in resolution and normalized to accelerate the training phase.To assess the network performance,a set of both synthetic and real images was inputted.After the preprocessing phase,real images were fine-tuned for vignette reduction and background brightness uniformity.Additionally,they are down-converted to eight bits.Once the network outputs labels,post-processing identifies the centroid right ascension and declination of the object.The average processing time per real image is less than 1.2 s;bright tracklets are easily detected with a mean centroid angular error of 0.25 deg in 75%of test cases with a 2 deg field-of-view telescope.These results prove that an ML-based method can be considered a valid choice when dealing with trail reconstruction,leading to acceptable accuracy for a fast image processing pipeline.
基金supported by International Science and Technology Cooperation Program of China(2019YFE0100200)in part by National Natural Science Foundation of China(61903220)National Natural Science Foundation of China(U1864203).
文摘Real driving scenarios,due to occlusions and disturbances,provide disordered and noisy measurements,which makes the task of multi-object tracking quite challenging.Conventional approach is to find deterministic data association;however,it has unstable performance in high clutter density.This paper proposes a novel probabilistic tracklet-enhanced multiple object tracker(PTMOT),which integrates Poisson multi-Bernoulli mixture(PMBM)filter with confidence of tracklets.The proposed method is able to realize efficient and robust probabilistic association for 3D multi-object tracking(MOT)and improve the PMBM filter’s continuity by smoothing single target hypothesis with global hypothesis.It consists of two key parts.First,the PMBM tracker based on sets of tracklets is implemented to realize probabilistic fusion of disordered measure-ments.Second,the confidence of tracklets is smoothed through a smoothing-while-filtering approach.Extensive MOT tests on nuScenes tracking dataset demonstrate that the proposed method achieves superior performance in different modalities.
