Papers Activity Recognition In Videos
“Activity Recognition In Videos” 태그가 달린 논문 18편 · 필터 해제
ActNetFormer: Transformer-ResNet Hybrid Method for Semi-Supervised Action Recognition in Videos
Human action or activity recognition in videos is a fundamental task in computer vision with applications in surveillance and monitoring, self-driving cars, sports analytics, human-robot interaction and many more. Tradit…
Action RecognitionAction Recognition In VideosActivity RecognitionActivity Recognition In Videos+4Dual-path Adaptation from Image to Video Transformers
In this paper, we efficiently transfer the surpassing representation power of the vision foundation models, such as ViT and Swin, for video understanding with only a few trainable parameters. Previous adaptation methods …
Action ClassificationAction RecognitionAction Recognition In VideosActivity Recognition+2Differentiable Frequency-based Disentanglement for Aerial Video Action Recognition
We present a learning algorithm for human activity recognition in videos. Our approach is designed for UAV videos, which are mainly acquired from obliquely placed dynamic cameras that contain a human actor along with bac…
Action RecognitionActivity RecognitionActivity Recognition In VideosDisentanglement+2ViT-ReT: Vision and Recurrent Transformer Neural Networks for Human Activity Recognition in Videos
Human activity recognition is an emerging and important area in computer vision which seeks to determine the activity an individual or group of individuals are performing. The applications of this field ranges from gener…
Activity RecognitionActivity Recognition In VideosGesture RecognitionHuman Activity RecognitionTorMentor: Deterministic dynamic-path, data augmentations with fractals
We propose the use of fractals as a means of efficient data augmentation. Specifically, we employ plasma fractals for adapting global image augmentation transformations into continuous local transforms. We formulate the …
Activity Recognition In VideosBinarizationData AugmentationGPU+6Long Term Object Detection and Tracking in Collaborative Learning Environments
Human activity recognition in videos is a challenging problem that has drawn a lot of interest, particularly when the goal requires the analysis of a large video database. AOLME project provides a collaborative learning …
Activity RecognitionActivity Recognition In VideosData AugmentationHand Detection+3Human Interaction Recognition Framework based on Interacting Body Part Attention
Human activity recognition in videos has been widely studied and has recently gained significant advances with deep learning approaches; however, it remains a challenging task. In this paper, we propose a novel framework…
Activity RecognitionActivity Recognition In VideosHuman Activity RecognitionHuman Interaction RecognitionBubblenet: A Disperse Recurrent Structure To Recognize Activities
This paper presents an approach to perform human activity recognition in videos through the employment of a deep recurrent network, taking as inputs appearance and optical flow information. Our method proposes a novel ar…
Action RecognitionActivity RecognitionActivity Recognition In VideosHuman Activity Recognition+1Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction
Spiking neural networks (SNNs) can be used in low-power and embedded systems (such as emerging neuromorphic chips) due to their event-based nature. Also, they have the advantage of low computation cost in contrast to con…
Activity Recognition In VideosEvent data classificationImage ClassificationVideo ClassificationLooking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction
In this paper, we present an end-to-end future-prediction model that focuses on pedestrian safety. Specifically, our model uses previous video frames, recorded from the perspective of the vehicle, to predict if a pedestr…
Action RecognitionActivity Recognition In VideosAutonomous DrivingDecoder+4Large-scale weakly-supervised pre-training for video action recognition
Current fully-supervised video datasets consist of only a few hundred thousand videos and fewer than a thousand domain-specific labels. This hinders the progress towards advanced video architectures. This paper presents …
Action ClassificationAction RecognitionActivity RecognitionActivity Recognition In Videos+3Combined Static and Motion Features for Deep-Networks Based Activity Recognition in Videos
Activity recognition in videos in a deep-learning setting---or otherwise---uses both static and pre-computed motion components. The method of combining the two components, whilst keeping the burden on the deep network le…
Activity RecognitionActivity Recognition In VideosRepresentation Flow for Action Recognition
In this paper, we propose a convolutional layer inspired by optical flow algorithms to learn motion representations. Our representation flow layer is a fully-differentiable layer designed to capture the `flow' of any rep…
Action ClassificationAction RecognitionAction Recognition In VideosActivity Recognition+5Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
In this paper, we newly introduce the concept of temporal attention filters, and describe how they can be used for human activity recognition from videos. Many high-level activities are often composed of multiple tempora…
Action ClassificationAction Recognition In VideosActivity RecognitionActivity Recognition In Videos+1Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors
Visual features are of vital importance for human action understanding in videos. This paper presents a new video representation, called trajectory-pooled deep-convolutional descriptor (TDD), which shares the merits of b…
Action RecognitionAction UnderstandingActivity Recognition In VideosTemporal Action LocalizationA new network-based algorithm for human activity recognition in video
In this paper, a new network-transmission-based (NTB) algorithm is proposed for human activity recognition in videos. The proposed NTB algorithm models the entire scene as an error-free network. In this network, each nod…
Action DetectionActivity DetectionActivity RecognitionActivity Recognition In Videos+2Pooled Motion Features for First-Person Videos
In this paper, we present a new feature representation for first-person videos. In first-person video understanding (e.g., activity recognition), it is very important to capture both entire scene dynamics (i.e., egomotio…
Activity RecognitionActivity Recognition In VideosTime SeriesTime Series Analysis+1Very Deep Convolutional Networks for Large-Scale Image Recognition
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using…
Activity Recognition In VideosClassificationDomain GeneralizationFace Anti-Spoofing+2