L-Shape Model Switching-Based Precise Motion Tracking of Moving Vehicles Using Laser Scanners

Kim, Dongchul; Jo, Kichun; Lee, Min Chul; Sunwoo, Myoungho

doi:10.1109/tits.2017.2771820

Cited by 44 publications

(25 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The variation of P c with regularization parameters α and γ at time step k = 50 is shown in Figure 4. It is observed that the proposed method performs best when α ∈ [5,7] and γ ∈ [10,20]. Here, we set α = 6, and γ = 15.…”

Section: Computer Simulationsmentioning

confidence: 99%

“…The track association problem is formulated as a maximum likelihood estimation problem with a local track geometry constraint, and an expectation-maximization (EM) algorithm is developed to find the solution. Simulation results demonstrate that the proposed methods offer better performance than the state-of-the-art methods.system in intelligent vehicles [7][8][9]. A multi-sensor multi-target tracking module jointly estimates the states and the number of targets from sensor measurements in intelligent vehicles, and it can be broadly categorized as centralized or distributed.…”

mentioning

confidence: 99%

“…system in intelligent vehicles [7][8][9]. A multi-sensor multi-target tracking module jointly estimates the states and the number of targets from sensor measurements in intelligent vehicles, and it can be broadly categorized as centralized or distributed.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Track-to-Track Association for Intelligent Vehicles by Preserving Local Track Geometry

Zhu

Freitas

et al. 2020

Sensors

View full text Add to dashboard Cite

Track-to-track association (T2TA) is a challenging task in situational awareness in intelligent vehicles and surveillance systems. In this paper, the problem of track-to-track association with sensor bias (T2TASB) is considered. Traditional T2TASB algorithms only consider a statistical distance cost between local tracks from different sensors, without exploiting the geometric relationship between one track and its neighboring ones from each sensor. However, the relative geometry among neighboring local tracks is usually stable, at least for a while, and thus helpful in improving the T2TASB. In this paper, we propose a probabilistic method, called the local track geometry preservation (LTGP) algorithm, which takes advantage of the geometry of tracks. Assuming that the local tracks of one sensor are represented by Gaussian mixture model (GMM) centroids, the corresponding local tracks of the other sensor are fitted to those of the first sensor. In this regard, a geometrical descriptor connectivity matrix is constructed to exploit the relative geometry of these tracks. The track association problem is formulated as a maximum likelihood estimation problem with a local track geometry constraint, and an expectation-maximization (EM) algorithm is developed to find the solution. Simulation results demonstrate that the proposed methods offer better performance than the state-of-the-art methods.system in intelligent vehicles [7][8][9]. A multi-sensor multi-target tracking module jointly estimates the states and the number of targets from sensor measurements in intelligent vehicles, and it can be broadly categorized as centralized or distributed. The advantage of the distributed tracking systems is that they can provide a degree of scalability and robustness not achievable by traditional centralized tracking systems [1].Track-to-track association (T2TA) is a crucial task in distributed tracking to find the correspondence between local tracks from different sensors. It is commonly applied to combine the local tracks of a sensor with those of another sensor to form the global tracklist. For automotive applications, radar, lidar, and video sensors in environmental perception systems for intelligent vehicles use different coordinate systems and sampling frequencies. Therefore, a spatio-temporal calibration should be performed to align the detections from different sensors [1]. In practice, detection from radar, lidar, and video sensors cannot always be calibrated or aligned accurately [10]. Each sensor may cover a different part of the surveillance region with a detection probability of less than one. As a result, some local tracks from a sensor may not correspond to those of other sensors. The range, azimuth, and elevation biases of a radar sensor may lead to errors in the local tracks from that sensor. The relationship between radar sensor bias and local tracks is presented in Figure 1, where two radar sensors, A and B, and one target, T, are shown. The radar sensor bias leads to the reporting of the target T as tracks T A and...

show abstract

Section: Computer Simulationsmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Track-to-Track Association for Intelligent Vehicles by Preserving Local Track Geometry

Zhu

Freitas

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Recently, three evaluation criteria were proposed in [16] within an optimization framework to generate a bounding box that can embrace all the points. In [17], the points were iteratively clustered to two orthogonal segments using their ordered information.…”

Section: Related Workmentioning

confidence: 99%

BoxNet: A Deep Learning Method for 2D Bounding Box Estimation from Bird's-Eye View Point Cloud

Nezhadarya

Liu²,

Liu³

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

We present a learning-based method to estimate the object bounding box from its 2D bird's-eye view (BEV) LiDAR points. Our method, entitled BoxNet, exploits a simple deep neural network that can efficiently handle unordered points. The method takes as input the 2D coordinates of all the points and the output is a vector consisting of both the box pose (position and orientation in LiDAR coordinate system) and its size (width and length). In order to deal with the angle discontinuity problem, we propose to estimate the double-angle sinusoidal values rather than the angle itself. We also predict the center relative to the point cloud mean to boost the performance of estimating the location of the box. The proposed method does not rely on the ordering of points as in many existing approaches, and can accurately predict the actual size of the bounding box based on the prior information that is obtained from the training data. BoxNet is validated using the KITTI 3D object dataset, with significant improvement compared with the state-of-the-art non-learning based methods.

show abstract

“…A common approach for object tracking is to fit boxes into raw measurements and track the shapes. However, these methods mainly use hand engineered features like L-shapes in laser [13], [14] or radar measurements [15]. L-shapes are a popular choice, because they resemble the front and side of a car, which can be seen by almost every sensor.…”

Section: Related Workmentioning

confidence: 99%

Deep Object Tracking on Dynamic Occupancy Grid Maps Using RNNs

Engel

Hoermann

Henzler

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

The comprehensive representation and understanding of the driving environment is crucial to improve the safety and reliability of autonomous vehicles. In this paper, we present a new approach to establish an environment model containing a segmentation between static and dynamic background and parametric modeled objects with shape, position and orientation. Multiple laser scanners are fused into a dynamic occupancy grid map resulting in a 360 • perception of the environment. A single-stage deep convolutional neural network is combined with a recurrent neural network, which takes a time series of the occupancy grid map as input and tracks cell states and its corresponding object hypotheses. The labels for training are created unsupervised with an automatic label generation algorithm.The proposed methods are evaluated in real-world experiments in complex inner city scenarios using the aforementioned 360 • laser perception. The results show a better object detection accuracy in comparison with our old approach as well as an AUC score of 0.946 for the dynamic and static segmentation. Furthermore, we gain an improved detection for occluded objects and a more consistent size estimation due to the usage of time series as input and the memory about previous states introduced by the recurrent neural network.

show abstract

L-Shape Model Switching-Based Precise Motion Tracking of Moving Vehicles Using Laser Scanners

Cited by 44 publications

References 24 publications

Track-to-Track Association for Intelligent Vehicles by Preserving Local Track Geometry

Track-to-Track Association for Intelligent Vehicles by Preserving Local Track Geometry

BoxNet: A Deep Learning Method for 2D Bounding Box Estimation from Bird's-Eye View Point Cloud

Deep Object Tracking on Dynamic Occupancy Grid Maps Using RNNs

Contact Info

Product

Resources

About