摘要 :
Driven by recent advances in object detection with deep neural networks, the tracking-by-detection paradigm has gained increasing prevalence in the research community of multi-object tracking (MOT). It has long been known that app...
展开
Driven by recent advances in object detection with deep neural networks, the tracking-by-detection paradigm has gained increasing prevalence in the research community of multi-object tracking (MOT). It has long been known that appearance information plays an essential role in the detection-to-track association, which lies at the core of the tracking-by-detection paradigm. While most existing works consider the appearance distances between the detections and the tracks, they ignore the statistical information implied by the historical appearance distance records in the tracks, which can be particularly useful when a detection has similar distances with two or more tracks. In this work, we propose a hybrid track association (HTA) algorithm that models the historical appearance distances of a track with an incremental Gaussian mixture model (IGMM) and incorporates the derived statistical information into the calculation of the detection-to-track association cost. Experimental results on three MOT benchmarks confirm that HTA effectively im proves the target identification performance with a small compromise to the tracking speed. Additionally, compared to many state-of-the-art trackers, the DeepSORT tracker equipped with HTA achieves better or comparable performance in terms of the balance of tracking quality and speed.(c) 2021 Elsevier B.V. All rights reserved.
收起
摘要 :
The paper addresses the problemof track-to-track association in the presence of sensor biases. In some challenging scenarios, itmay be infeasible to implement bias estimation and compensation in time due to the computational intra...
展开
The paper addresses the problemof track-to-track association in the presence of sensor biases. In some challenging scenarios, itmay be infeasible to implement bias estimation and compensation in time due to the computational intractability or weak observability about sensor biases. In this paper, we introduce the structural feature for each local track, which describes the spatial relationship with its neighboring targets. Although the absolute coordinates of local tracks from the same target are severely different in the presence of sensor biases, their structural features may be similar. As a result, instead of using the absolute kinematic states only, we employee the structural similarity to define the association cost. When there are missed detections, the structural similarity between local tracks is evaluated by solving another 2D assignment subproblem. Simulation results demonstrated the power of the proposed approach.
收起
摘要 :
The formula for calculating the theoretical probability of false association for the technique of track-to-track association (TTTA) using multiscan data is presented. The multiscan TTTA technique and the single-scan TTTA technique...
展开
The formula for calculating the theoretical probability of false association for the technique of track-to-track association (TTTA) using multiscan data is presented. The multiscan TTTA technique and the single-scan TTTA technique are studied by analysis and simulation. The conclusion is that the theoretical performance of the multiscan method is better than that of the single-scan method; however the practical performance of the former evaluated in our simulation is much worse than its theoretical performance, yet could still be more or less better than that of the latter. The reason is that estimation errors of the sensor-level tracks are strongly dependent across time. (C) 2004 Published by Elsevier B.V.
收起
摘要 :
The formula for calculating the theoretical probability of false association for the technique of track-to-track association (TTTA) using multiscan data is presented. The multiscan TTTA technique and the singlescan TTTA technique ...
展开
The formula for calculating the theoretical probability of false association for the technique of track-to-track association (TTTA) using multiscan data is presented. The multiscan TTTA technique and the singlescan TTTA technique are studied by analysis and simulation. The conclusion is that the theoretical performance of the multiscan method is better than that of the singlescan method; however the practical performance of the former evaluated in our simulation is much worse than its theoretical performance, yet could still be more or less better than that of the latter. The reason is that estimation errors of the sensor-level tracks are strongly dependent across time. (c) 2005 Elsevier B.V. All rights reserved.
收起
摘要 :
Track-to-track association (T2TA) aims at unifying batch numbers of tracks, reducing redundancy, and clarifying the situation. It is the precondition and foundation of track fusion, situation awareness, and traffic control. Existi...
展开
Track-to-track association (T2TA) aims at unifying batch numbers of tracks, reducing redundancy, and clarifying the situation. It is the precondition and foundation of track fusion, situation awareness, and traffic control. Existing methods based on statistical reasoning or fuzzy math bring some problems that are difficult to solve simultaneously, such as unreasonable assumptions, unsuitable models, uncertain thresholds, and long association times. In the light of the above problems, in this paper, we proposed a T2TA method via Track Fusion and Track Segmentation (TF-TS). The track fusion module fuses and extracts track features from several tracks to reduce the dependency on prior assumptions, motion models, and thresholds. The track segmentation mapping module transforms track tensors into association matrices directly to improve association efficiency. With four kinds of constraints, the association matrices inferred by TF-TS are close to the real one. TF-TS can reduce the dependence on the assumptions, motion models, and thresholds. It can also reduce the traversal calculation of tracks and increase the association efficiency. The global AIS tracks are used to train and test our model, and association results demonstrate that the proposed method can associate tracks in complex scenarios. Moreover, the efficiency is improved and the demand for the real-time association is satisfied.
收起
摘要 :
The detection and identification of Resident Space Objects (RSOs) from survey tracks requires robust and efficient orbit determination methods for the association of observations of the same RSO. Both Initial Orbit Determination (...
展开
The detection and identification of Resident Space Objects (RSOs) from survey tracks requires robust and efficient orbit determination methods for the association of observations of the same RSO. Both Initial Orbit Determination (IOD) and Orbit Determination (OD) methods perform the orbital estimation in which the association of tracks relies. The choice of proper IOD and OD methods is essential for the whole data association, since they are in charge of providing the estimation required to evaluate the figure of merit of the association. In this paper, we review the state of the art and propose a novel method that does not require initialisation, accounts for measurement noise and provides a full estimation (i.e., state vector and covariance) from an arbitrary number of optical observations. To do so, a boundary value problem is formulated to find a pair of ranges leading to a minimum residuals of the observations. The proposed methods are compared against classical alternatives simulated in scenarios representative of the current space debris environment.
收起
摘要 :
Local tracking in clutter initialises and updates true and false tracks. Local false track discrimination uses a track quality measure to confirm most of the true tracks, and to terminate most of the false tracks. Confirmed tracks...
展开
Local tracking in clutter initialises and updates true and false tracks. Local false track discrimination uses a track quality measure to confirm most of the true tracks, and to terminate most of the false tracks. Confirmed tracks are transmitted for track-to-track fusion. The sets of tracks being considered for fusion may contain both true and false tracks. The authors assume that each track information also includes the track quality measure in the form of the probability of target existence information. This information is used for additional false track discrimination at the fusion centre. They also use this information to enhance the track-to-track association. They propose three different strategies for track fusion with the target existence information: the `single target', the `joint multitarget' and the `linear multitarget'.
收起
摘要 :
Due to sensor characteristics, geographical environment, electromagnetic interference, electromagnetic silence, information countermeasures, and other reasons, there may be significant system errors in sensors in multi-sensor trac...
展开
Due to sensor characteristics, geographical environment, electromagnetic interference, electromagnetic silence, information countermeasures, and other reasons, there may be significant system errors in sensors in multi-sensor tracking systems, resulting in poor track-to-track association (TTTA) effect of the system. In order to solve the problem of TTTA under large system errors, this paper proposes an asynchronous anti-bias TTTA algorithm that utilizes the average distance between the nearest neighbor intervals between tracks. This algorithm proposes a systematic error interval processing method to track coordinates, and then defines the nearest neighbor interval average distance between interval coordinate datasets and interval coordinate points, and then uses grey theory to calculate the correlation degree between tracks. Finally, the Jonker-Volgenant algorithm is combined to use the canonical allocation method for TTTA judgment. The algorithm requires less prior information and does not require error registration. The simulation results show that the algorithm can ensure a high average correct association rate (over 98%) of asynchronous unequal rate tracks under large system errors, and achieve stable association, with good association and anti-bias performance. Compared with other algorithms, the algorithm maintains good performance for different target numbers and processing cycles, and has good superiority and robustness.
收起
摘要 :
In radar network systems, target tracks reported from different radars need to be associated and fused, and the track-to-track association (TTTA) effect is a key factor that directly affects the performance of the entire system. I...
展开
In radar network systems, target tracks reported from different radars need to be associated and fused, and the track-to-track association (TTTA) effect is a key factor that directly affects the performance of the entire system. In order to solve the problem of the low accuracy of TTTA in network radar systems with asynchronous unequal rates, an asynchronous TTTA algorithm based on pseudo nearest neighbor distance is proposed. Firstly, the calculation method of pseudo nearest neighbor distance between the track point and the track data set is defined, then the correlation degree between the two track data sets is obtained by using grey theory, and then the Jonker-Volgenant algorithm is combined with the classical allocation method to judge the TTTA. The algorithm does not need time domain alignment and can effectively avoid the accumulation and propagation of estimation errors. The simulation results show that the algorithm has a high average correct association rate and is less affected by the radar sampling period ratio, startup time, and noise distribution, and the average correct association rate for different movement types of target tracks remains above 99%. Furthermore, compared with other algorithms, this algorithm maintains a stable low level of the number of false associations and the maximum false association rates, and has strong robustness and advantages.
收起
摘要 :
Over-the-Horizon Radar (OTHR) exploits the refraction of high frequency radiation through the ionosphere layers to detect targets beyond the line-of-sight horizon. Multipath propagation between the radar and the detected targets m...
展开
Over-the-Horizon Radar (OTHR) exploits the refraction of high frequency radiation through the ionosphere layers to detect targets beyond the line-of-sight horizon. Multipath propagation between the radar and the detected targets may results in multiple spatially separated tracks for a single target to be observed at the receiver site. Consequently there is a heavy traffic, especially in case of multiple targets, to be associated and combined if there are tracks represent the same target. In this study, a new method for multipath clustering for OTHR is proposed. The proposed method describes the similarities between all tracks as fuzzy degrees of membership. This method can operate in real-time and can perform clustering and fusion of OTHR tracks with tracks from other sources such as targets reporting global positioning systems and microwave radars. The proposed method has the advantages of less computations and high efficiency compared to conventional fuzzy logic clustering techniques. It has also the advantage of treating all the tracks data at once rather than pairwise. The efficiency of the proposed method is demonstrated using simulated examples.
收起
原文获取