Slot Cars: 3D Modelling for Improved Visual Traffic Analytics

Corral-Soto, Eduardo R.; Elder, James H.

doi:10.1109/cvprw.2017.123

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…object detectors -either based on convolutional neural networks [55], [56], AdaBoost [57], Deformable Part Models [42], [58] or Hough Transformation [59]. There were also attempts to improve specifically vehicle detection based on geometric information [60], during night [61], or to increase the accuracy of localization of occluded vehicles [62].…”

Section: Vehicle Detectionmentioning

confidence: 99%

BoxCars: Improving Fine-Grained Recognition of Vehicles Using 3-D Bounding Boxes in Traffic Surveillance

Sochor

Špaňhel

Herout

2019

IEEE Trans. Intell. Transport. Syst.

114

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In this paper, we focus on fine-grained recognition of vehicles mainly in traffic surveillance applications. We propose an approach that is orthogonal to recent advancements in finegrained recognition (automatic part discovery, bilinear pooling). Also, in contrast to other methods focused on fine-grained recognition of vehicles, we do not limit ourselves to a frontal/rear viewpoint, but allow the vehicles to be seen from any viewpoint. Our approach is based on 3D bounding boxes built around the vehicles. The bounding box can be automatically constructed from traffic surveillance data. For scenarios where it is not possible to use precise construction, we propose a method for an estimation of the 3D bounding box. The 3D bounding box is used to normalize the image viewpoint by "unpacking" the image into a plane. We also propose to randomly alter the color of the image and add a rectangle with random noise to a random position in the image during the training of Convolutional Neural Networks. We have collected a large fine-grained vehicle dataset BoxCars116k, with 116k images of vehicles from various viewpoints taken by numerous surveillance cameras. We performed a number of experiments which show that our proposed method significantly improves CNN classification accuracy (the accuracy is increased by up to 12 percentage points and the error is reduced by up to 50 % compared to CNNs without the proposed modifications). We also show that our method outperforms stateof-the-art methods for fine-grained recognition.

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Section: Vehicle Detectionmentioning

confidence: 99%

BoxCars: Improving Fine-Grained Recognition of Vehicles Using 3-D Bounding Boxes in Traffic Surveillance

Sochor

Špaňhel

Herout

2019

IEEE Trans. Intell. Transport. Syst.

114

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“…1) MCMC proposals: In MCMC a proposal distrubution is selected from which to draw samples of the proposed parameters. Inspired by [27], in our problem we design the proposal distribution as follows:…”

Section: The Question Now Is What Values Of ⃗ S and ⃗mentioning

confidence: 99%

Domain Adaptation in LiDAR Semantic Segmentation via Hybrid Learning with Alternating Skip Connections

Corral-Soto

Rochan

et al. 2023

2023 IEEE Intelligent Vehicles Symposium (IV)

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In recent years, much progress has been made in LiDAR-based 3D object detection mainly due to advances in detector architecture designs and availability of large-scale LiDAR datasets. Existing 3D object detectors tend to perform well on the point cloud regions closer to the LiDAR sensor as opposed to on regions that are farther away. In this paper, we investigate this problem from the data perspective instead of detector architecture design. We observe that there is a learning bias in detection models towards the dense objects near the sensor and show that the detection performance can be improved by simply manipulating the input point cloud density at different distance ranges without modifying the detector architecture and without data augmentation. We propose a modelfree point cloud density adjustment pre-processing mechanism that uses iterative MCMC optimization to estimate optimal parameters for altering the point density at different distance ranges. We conduct experiments using four state-of-the-art LiDAR 3D object detectors on two public LiDAR datasets, namely Waymo and ONCE. Our results demonstrate that our range-based point cloud density manipulation technique can improve the performance of the existing detectors, which in turn could potentially inspire future detector designs.

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“…Background subtraction is a common method of detecting vehicles as the traffic surveillance cameras are static. Corral-Soto and Elder [2] fit a mixture model for the distribution of vehicle dimensions on labeled data. The model is used together with the known geometry of the scene to estimate the vehicle configuration for blobs of vehicles obtained via background subtraction.…”

Section: Vehicle Detectionmentioning

confidence: 99%

Detection of 3D Bounding Boxes of Vehicles Using Perspective Transformation for Accurate Speed Measurement

Kocur,

Ftáčnik

2020

Preprint

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Detection and tracking of vehicles captured by traffic surveillance cameras is a key component of intelligent transportation systems. We present an improved version of our algorithm for detection of 3D bounding boxes of vehicles, their tracking and subsequent speed estimation. Our algorithm utilizes the known geometry of vanishing points in the surveilled scene to construct a perspective transformation. The transformation enables an intuitive simplification of the problem of detecting 3D bounding boxes to detection of 2D bounding boxes with one additional parameter using a standard 2D object detector. Main contribution of this paper is an improved construction of the perspective transformation which is more robust and fully automatic and an extended experimental evaluation of speed estimation. We test our algorithm on the speed estimation task of the BrnoCompSpeed dataset. We evaluate our approach with different configurations to gauge the relationship between accuracy and computational costs and benefits of 3D bounding box detection over 2D detection. All of the tested configurations run in real-time and are fully automatic. Compared to other published state-of-the-art fully automatic results our algorithm reduces the mean absolute speed measurement error by 32% (1.10 km/h to 0.75 km/h) and the absolute median error by 40% (0.97 km/h to 0.58 km/h).

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Slot Cars: 3D Modelling for Improved Visual Traffic Analytics

Cited by 9 publications

References 14 publications

BoxCars: Improving Fine-Grained Recognition of Vehicles Using 3-D Bounding Boxes in Traffic Surveillance

BoxCars: Improving Fine-Grained Recognition of Vehicles Using 3-D Bounding Boxes in Traffic Surveillance

Domain Adaptation in LiDAR Semantic Segmentation via Hybrid Learning with Alternating Skip Connections

Detection of 3D Bounding Boxes of Vehicles Using Perspective Transformation for Accurate Speed Measurement

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