Vehicle counting based on a stereo vision depth maps for parking management

Lee, Meng-Rong; Lin, Daw-Tung

doi:10.1007/s11042-018-6394-6

Cited by 8 publications

(5 citation statements)

References 21 publications

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“…Depth estimation could be used in multiple applications for C-ITS, such as autonomous navigation [11], parking management [12], and traffic flow estimation [13]. These approaches typically use depth map estimations for generating the 3D geometry of a scene.…”

Section: A Depth Estimationmentioning

confidence: 99%

“…We have chosen these models due to the following reasons: (1) depth estimation from a single image is a complex task that requires deep models, in which we could showcase the compression ability of CCMC, (2) monocular images are more common than other modalities that use specialized hardware (e.g., point clouds from LiDAR and stereo images). (3) In the context of a roadside camera, depth estimation offers a 3D understanding of a scene, which is useful for other traffic-related tasks (e.g., navigation [10], [11], parking management [12], and traffic flow estimation [13]). ( 4) Compressed depth estimation models have reduced fidelity, however, they could still be applied for smart surveillance of simple environments (e.g., few scene objects), see Figure 1.…”

Section: Introductionmentioning

confidence: 99%

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Controllable Model Compression for Roadside Camera Depth Estimation

Ople,

Chen,

Chen

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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In the Cooperative Intelligent Transportation System (C-ITS) paradigm, vehicles could communicate with roadside units to augment their traffic knowledge. Smart roadside units could provide second-order information (e.g., vehicle count) from raw first-order data (e.g., visual feed, point clouds), and this "smart" feature is usually provided using deep neural network models. However, implementing these useful models implies a cost for computational complexity that could hinder the future deployment of smart roadside units needed for sustainability in transportation systems. In this paper, we propose to use model compression on deep image processing models to promote its feasibility for usage in smart sensors. We formulated a controllable convolutional model compression (CCMC) algorithm that can perform filter-wise evolutionary pruning on image processing networks, along with a predefined compression ratio. CCMC is applicable for image processing networks, which have multiple possible traffic data sources (e.g., road camera surveillance). Furthermore, CCMC has a definable target compression ratio that is useful for controlling the trade-off between resource consumption and output performance. We tested our proposed method on depth estimation, which is useful for scene understanding and mapping the locations of objects in the 3D space. Our experiments show that the pruned model has minimal performance discrepancy from the original one, supporting the sustainability features needed for intelligent transportation systems.

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Section: A Depth Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Controllable Model Compression for Roadside Camera Depth Estimation

Ople,

Chen,

Chen

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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show abstract

“…Despite the significant achievements in terms of accuracy, modern state-of-the-art techniques for monocular depth estimation [10], [12], [13] are overkill for many edge applications. Specifically, while high-resolution dense depth maps are desirable when dealing with tasks such as 3D reconstruction and SLAM, a rough depth estimate suffices in many applications such as object/people counting [14], [15], pose estimation [16], action recognition [5], vehicle detection [17]. Indeed, millimetric depth measurements are not strictly required in these cases to tackle the problem successfully.…”

Section: Motivations and Practical Use-casesmentioning

confidence: 99%

Monocular Depth Perception on Microcontrollers for Edge Applications

Peluso

Cipolletta

Calimera

et al. 2022

IEEE Trans. Circuits Syst. Video Technol.

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Depth estimation is crucial in several computer vision applications, and a recent trend in this field aims at inferring such a cue from a single camera. Unfortunately, despite the compelling results achieved, state-of-the-art monocular depth estimation methods are computationally demanding, thus precluding their practical deployment in several application contexts characterized by low-power constraints. Therefore, in this paper, we propose a lightweight Convolutional Neural Network based on a shallow pyramidal architecture, referred to as µPyD-Net, enabling monocular depth estimation on microcontrollers. The network is trained in a peculiar self-supervised manner leveraging proxy labels obtained through a traditional stereo algorithm. Moreover, we propose optimization strategies aimed at performing computations with quantized 8-bit data and map the high-level description of the network to low-level layers optimized for the target microcontroller architecture. Exhaustive experimental results on standard datasets and an in-depth evaluation with a device belonging to the popular Arm Cortex-M family confirm that obtaining sufficiently accurate monocular depth estimation on microcontrollers is feasible. To the best of our knowledge, our proposal is the first one enabling such remarkable achievement, paving the way for the deployment of monocular depth cues onto the tiny end-nodes of distributed sensor networks.

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“…e negative sample set includes various background scenes without vehicles that may appear around the road. For example, there may be road signs, tree shades, pedestrians, trees, etc., which are also sufficiently diverse [20,21].…”

Section: Sample Collectionmentioning

confidence: 99%

Vehicle Image Detection Method Using Deep Learning in UAV Video

Wang

2022

Computational Intelligence and Neuroscience

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Traditional machine learning algorithms are susceptible to objective factors such as video quality and weather environment in the vehicle detection of Unmanned Aerial Vehicle (UAV) videos, resulting in poor detection results. A vehicle image detection method using deep learning in UAV video is proposed. The algorithm in this paper treats surveillance video as many frames of images for vehicle detection in the image. First, perform HSV (Hue-Saturation-Value) spatial brightness translation operation on the original sample to increase the adaptability to different light conditions and sample diversity. Then, the Single Shot MultiBox Detector (SSD) model framework is used as the basis for vehicle detection. In order to obtain a better feature extraction effect, focus loss is added to the basic SSD for optimization. Finally, the trained network model is used to analyze the UAV video, and the detection performance is analyzed experimentally. The results show that the vehicle detection rate of this algorithm has reached 96.49%. It can ensure that the vehicle is accurately detected from the drone video.

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Vehicle counting based on a stereo vision depth maps for parking management

Cited by 8 publications

References 21 publications

Controllable Model Compression for Roadside Camera Depth Estimation

Controllable Model Compression for Roadside Camera Depth Estimation

Monocular Depth Perception on Microcontrollers for Edge Applications

Vehicle Image Detection Method Using Deep Learning in UAV Video

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