YOLO Series for Human Hand Action Detection and Classification from Egocentric Videos

Nguyen, Hung‐Cuong; Nguyen, Thi-Hao; Scherer, Rafał; Le, Van-Hung

doi:10.3390/s23063255

Cited by 8 publications

(4 citation statements)

References 46 publications

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“…The dataset provides annotations for panoptic and motion segmentation, 3D hand pose, category-level object pose and includes reconstructed object meshes and scene point clouds. This dataset has proven useful for object segmentation and shape reconstruction (Liu et al, 2023c;Zhang et al, 2023d;Wen et al, 2022), action segmentation (Reza et al, 2023;Zhang et al, 2023d), hand-object manipulation synthesis (Zheng et al, 2023a;Ye et al, 2023b), hand action detection (Hung-Cuong et al, 2023) and 3D hand pose estimation (Ye et al, 2023b).…”

Section: General Datasetsmentioning

confidence: 99%

An Outlook into the Future of Egocentric Vision

Plizzari,

Goletto,

Furnari

et al. 2024

Int J Comput Vis

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What will the future be? We wonder! In this survey, we explore the gap between current research in egocentric vision and the ever-anticipated future, where wearable computing, with outward facing cameras and digital overlays, is expected to be integrated in our every day lives. To understand this gap, the article starts by envisaging the future through character-based stories, showcasing through examples the limitations of current technology. We then provide a mapping between this future and previously defined research tasks. For each task, we survey its seminal works, current state-of-the-art methodologies and available datasets, then reflect on shortcomings that limit its applicability to future research. Note that this survey focuses on software models for egocentric vision, independent of any specific hardware. The paper concludes with recommendations for areas of immediate explorations so as to unlock our path to the future always-on, personalised and life-enhancing egocentric vision.

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Section: General Datasetsmentioning

confidence: 99%

An Outlook into the Future of Egocentric Vision

Plizzari,

Goletto,

Furnari

et al. 2024

Int J Comput Vis

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“…It predicts the final target box by generating a set of candidate boxes, and then classifying and regressing these boxes. The second type is the one-stage detection algorithm using regression, with YOLO as its typical representative [8][9][10][11]. It directly convolves and pools the image to generate candidate boxes, and performs classification and regression at the same time to detect the vehicle object.…”

Section: Related Workmentioning

confidence: 99%

A Real-Time Small Target Vehicle Detection Algorithm with an Improved YOLOv5m Network Model

Du,

Jiang

2024

CMC

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To address the challenges of high complexity, poor real-time performance, and low detection rates for small target vehicles in existing vehicle object detection algorithms, this paper proposes a real-time lightweight architecture based on You Only Look Once (YOLO) v5m. Firstly, a lightweight upsampling operator called Content-Aware Reassembly of Features (CARAFE) is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles, reducing the missed detection rate and false detection rate. Secondly, a new prediction layer for tiny targets is added, and the feature fusion network is redesigned to enhance the detection capability for small targets. Finally, this paper applies L1 regularization to train the improved network, followed by pruning and fine-tuning operations to remove redundant channels, reducing computational and parameter complexity and enhancing the detection efficiency of the network. Training is conducted on the VisDrone2019-DET dataset. The experimental results show that the proposed algorithm reduces parameters and computation by 63.8% and 65.8%, respectively. The average detection accuracy improves by 5.15%, and the detection speed reaches 47 images per second, satisfying real-time requirements. Compared with existing approaches, including YOLOv5m and classical vehicle detection algorithms, our method achieves higher accuracy and faster speed for real-time detection of small target vehicles in edge computing.

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“…The control systems, navigation algorithm, and objection detection algorithms utilized in various agricultural and non-agricultural robots [5][6][7][8][9][10][11][12][13] can also be adapted for cotton harvesting robots. Several research articles have focused on distinct sub-components of a robotic cotton harvesting system, such as the development of a cotton boll detection model [14][15][16][17][18][19], navigation and path planning algorithms [20][21][22][23][24][25], and end-effector designs [26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Robotic Multi-Boll Cotton Harvester System Integration and Performance Evaluation

Thapa,

Rains,

Porter

et al. 2024

AgriEngineering

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Several studies on robotic cotton harvesters have designed their end-effectors and harvesting algorithms based on the approach of harvesting a single cotton boll at a time. These robotic cotton harvesting systems often have slow harvesting times per boll due to limited computational speed and the extended time taken by actuators to approach and retract for picking individual cotton bolls. This study modified the design of the previous version of the end-effector with the aim of improving the picking ratio and picking time per boll. This study designed and fabricated a pullback reel to pull the cotton plants backward while the rover harvested and moved down the row. Additionally, a YOLOv4 cotton detection model and hierarchical agglomerative clustering algorithm were implemented to detect cotton bolls and cluster them. A harvesting algorithm was then developed to harvest the cotton bolls in clusters. The modified end-effector, pullback reel, vacuum conveying system, cotton detection model, clustering algorithm, and straight-line path planning algorithm were integrated into a small red rover, and both lab and field tests were conducted. In lab tests, the robot achieved a picking ratio of 57.1% with an average picking time of 2.5 s per boll. In field tests, picking ratio was 56.0%, and it took an average of 3.0 s per boll. Although there was no improvement in the lab setting over the previous design, the robot’s field performance was significantly better, with a 16% higher picking ratio and a 46% reduction in picking time per boll compared to the previous end-effector version tested in 2022.

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YOLO Series for Human Hand Action Detection and Classification from Egocentric Videos

Cited by 8 publications

References 46 publications

An Outlook into the Future of Egocentric Vision

An Outlook into the Future of Egocentric Vision

A Real-Time Small Target Vehicle Detection Algorithm with an Improved YOLOv5m Network Model

Robotic Multi-Boll Cotton Harvester System Integration and Performance Evaluation

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