Simple Baseline for Vehicle Pose Estimation: Experimental Validation

Sanchez, Hector; Martínez, Antonio Martínez; Gonzalo, Rubén Izquierdo; Parra, Noelia Hernández; Alonso, Ignacio Parra; Llorca, David Fernández

doi:10.1109/access.2020.3010307

Cited by 7 publications

(2 citation statements)

References 48 publications

(78 reference statements)

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“…However, Stacked Hourglass has some limitations, such as incomplete retention of high‐resolution information. To overcome this issue and improve accuracy, a study [34] used the SimpleBaseline network. VehiPose [35] employs the Waterfall Atrous Spatial Pooling architecture (WASP) [36] in semantic segmentation, enabling more spatially detailed features and efficient multi‐scale feature extraction.…”

Section: Related Workmentioning

confidence: 99%

SEHRNet: A lightweight, high‐resolution network for aircraft keypoint detection

Zhang,

Zhu

et al. 2024

IET Image Processing

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Current research on apron conflict detection is often limited to the interaction between the aircraft as a whole and other objects, making it difficult to accomplish targeted identification of vulnerable and high‐cost aircraft components. However, the implementation of detailed aircraft identification is of great significance to enhance the safety of airport surface operations. Based on the excellent performance of High‐Resolution Network (HRNet) in keypoint detection, a lightweight end‐to‐end keypoint detection network, namely Squeeze and Excitation High‐Resolution Network (SEHRNet), is proposed in this paper to solve the problems of HRNet's slower computation and more redundancy. First, the errors arising from coordinate transformations in the coding and decoding process are solved by an improved feature map coding and decoding process. Second, replace the BasicBlock in HRNet with the Depthwise separable convolutions based on the Squeeze‐and‐Excitation Networks, which drastically cuts the computational cost of the network. Third, the improved Bottleneck module is used to further enhance the capability of feature extraction. Experimental results prove that, based on the aircraft keypoint detection dataset, the SEHRNet proposed in this paper shows stronger applicability compared to the current mainstream networks. Compared with the original HRNet, the improved network has higher accuracy, faster speed, and lighter model for aircraft keypoint detection.

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Section: Related Workmentioning

confidence: 99%

SEHRNet: A lightweight, high‐resolution network for aircraft keypoint detection

Zhang,

Zhu

et al. 2024

IET Image Processing

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“…Occlusion-net [34] uses a 3D graph network with self-supervision to predict 2D and 3D keypoints of vehicles using the CarFusion dataset [45], while GSNet [46] predicts 6DoF car pose and reconstructs dense 3D shape simultaneously. Without 3D information, the popular OpenPose [47] shows qualitative results for vehicles and Simple Baseline [48] extends a top-down pose estimator for cars on a custom dataset based on Pascal3D+ [49].…”

Section: Beyond Humansmentioning

confidence: 99%

OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association

Kreiss¹,

Bertoni²,

Alahi³

2021

Preprint

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Many image-based perception tasks can be formulated as detecting, associating and tracking semantic keypoints, e.g., human body pose estimation and tracking. In this work, we present a general framework that jointly detects and forms spatio-temporal keypoint associations in a single stage, making this the first real-time pose detection and tracking algorithm. We present a generic neural network architecture that uses Composite Fields to detect and construct a spatio-temporal pose which is a single, connected graph whose nodes are the semantic keypoints (e.g., a person's body joints) in multiple frames. For the temporal associations, we introduce the Temporal Composite Association Field (TCAF) which requires an extended network architecture and training method beyond previous Composite Fields. Our experiments show competitive accuracy while being an order of magnitude faster on multiple publicly available datasets such as COCO, CrowdPose and the PoseTrack 2017 and 2018 datasets. We also show that our method generalizes to any class of semantic keypoints such as car and animal parts to provide a holistic perception framework that is well suited for urban mobility such as self-driving cars and delivery robots.

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Vision‐based vehicle speed estimation: A survey

Llorca

Martínez

Daza

2021

IET Intelligent Trans Sys

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The need to accurately estimate the speed of road vehicles is becoming increasingly important for at least two main reasons. First, the number of speed cameras installed worldwide has been growing in recent years, as the introduction and enforcement of appropriate speed limits are considered one of the most effective means to increase the road safety. Second, traffic monitoring and forecasting in road networks plays a fundamental role to enhance traffic, emissions and energy consumption in smart cities, being the speed of the vehicles one of the most relevant parameters of the traffic state. Among the technologies available for the accurate detection of vehicle speed, the use of vision‐based systems brings great challenges to be solved, but also great potential advantages, such as the drastic reduction of costs due to the absence of expensive range sensors, and the possibility of identifying vehicles accurately. This paper provides a review of vision‐based vehicle speed estimation. The terminology and the application domains are described and a complete taxonomy of a large selection of works that categorizes all stages involved is proposed. An overview of performance evaluation metrics and available datasets is provided. Finally, current limitations and future directions are discussed.

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Simple Baseline for Vehicle Pose Estimation: Experimental Validation

Cited by 7 publications

References 48 publications

SEHRNet: A lightweight, high‐resolution network for aircraft keypoint detection

SEHRNet: A lightweight, high‐resolution network for aircraft keypoint detection

OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association

Vision‐based vehicle speed estimation: A survey

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