Multi-scale prediction for robust hand detection and classification
In this paper, we present a multi-scale Fully Convolutional Networks (MSP-RFCN) to robustly detect and classify human hands under various challenging conditions. In our approach, the input image is passed through the proposed network to generate score maps, based on multi-scale predictions. The network has been specifically designed to deal with small objects. It uses an architecture based on region proposals generated at multiple scales. Our method is evaluated on challenging hand datasets, namely the Vision for Intelligent Vehicles and Applications (VIVA) Challenge and the Oxford hand dataset. It is compared against recent hand detection algorithms. The experimental results demonstrate that our proposed method achieves state-of-the-art detection for hands of various sizes.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral ClassificationHand DetectionSimilar Papers 제목 키워드 기반
A Gaussian Scale Space Approach For Exudates Detection, Classification And Severity Prediction
In the context of Computer Aided Diagnosis system for diabetic retinopathy, we present a novel method for detection of exudates and their classification for disease severity prediction. The method is based on Gaussian sc…
General ClassificationPredictionSensitivityseverity predictionSimultaneous prediction of hand gestures, handedness, and hand keypoints using thermal images
Hand gesture detection is a well-explored area in computer vision with applications in various forms of Human-Computer Interactions. In this work, we propose a technique for simultaneous hand gesture classification, hand…
Multi-Task LearningMulti-Scale Positive Sample Refinement for Few-Shot Object Detection
Few-shot object detection (FSOD) helps detectors adapt to unseen classes with few training instances, and is useful when manual annotation is time-consuming or data acquisition is limited. Unlike previous attempts that e…
Few-Shot Object DetectionObjectobject-detectionObject DetectionDeep-FExt: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction
Feature extraction is a very crucial task in image and pixel (voxel) classification and regression in biomedical image modeling. In this work we present a machine learning based feature extraction scheme based on incepti…
ClassificationGeneral ClassificationXMTC: Explainable Early Classification of Multivariate Time Series in Reach-to-Grasp Hand Kinematics
Hand kinematics can be measured in Human-Computer Interaction (HCI) with the intention to predict the user's intention in a reach-to-grasp action. Using multiple hand sensors, multivariate time series data are being capt…
Early ClassificationTime Series