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Cascaded interactional targeting network for egocentric video analysis

Cascaded interactional targeting network for egocentric vide...

引用

2016 ieee conference on computer vision and pattern recognition, CVPR 2016

作者： Zhou, Yang Ni, Bingbing Hong, Richang Yang, Xiaokang Tian, Qi University of Texas San Antonio United States Shanghai Jiao Tong University China HeFei University of Technology China

ISBN: (纸本)9781467388511

Knowing how hands move and what object is being manipulated are two key sub-tasks for analyzing first-person (egocentric) action. However, lack of fully annotated hand data as well as imprecise foreground segmentation make either sub-task challenging. This work aims to explicitly address these two issues via introducing a cascaded interactional targeting (i.e., infer both hand and active object regions) deep neural network. Firstly, a novel EM-like learning framework is proposed to train the pixel-level deep convolutional neural network (DCNN) by seamlessly integrating weakly supervised data (i.e., massive bounding box annotations) with a small set of strongly supervised data (i.e., fully annotated hand segmentation maps) to achieve stateof-the-art hand segmentation performance. Secondly, the resulting high-quality hand segmentation maps are further paired with the corresponding motion maps and object feature maps, in order to explore the contextual information among object, motion and hand to generate interactional foreground regions (operated objects). The resulting interactional target maps (hand + active object) from our cascaded DCNN are further utilized to form discriminative action representation. Experiments show that our framework has achieved the state-of-the-art egocentric action recognition performance on the benchmark dataset Activities of Daily Living (ADL).

关键词： pattern recognition

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conference organization

Conference organization

引用

conference on computer vision and pattern recognition (CVPR)

Provides a listing of current committee members and society officers.

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Fusion of local appearance with stereo depth for object tracking

Fusion of local appearance with stereo depth for object trac...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Feng Tang Michael Harville Hai Tao Ian N. Robinson University of California Santa Cruz Santa Cruz CA US HP Laboratories Palo Alto CA USA University of California Santa Cruz CA USA

Object tracking methods based on stereo cameras, which provide both color and depth data at each pixel, find advantage in separating objects from each other and from background, determining the 3D size and location of objects, and modeling object shape. However, stereo tracking methods to date sometimes fail due to depth image noise, and discard much useful appearance information. We propose augmenting stereo-based models of tracked objects with sparse local appearance features, which have recently been applied with great success to object recognition under pose variation and partial occlusion. Depth data complements sparse local features by informing correct assignment of features to objects, while tracking of stable local appearance features helps overcome distortion of object shape models due to depth noise and partial occlusion. To speed up tracking of many local features, we also use a ldquobinary Gaborrdquo representation that is highly descriptive yet efficiently computed using integral images. In addition, a novel online feature selection and pruning technique is described to focus tracking onto the best localized and most consistent features. A tracking framework fusing all of these aspects is provided, and results for challenging video sequences are discussed.

关键词： Cameras Shape Object recognition Stereo vision Noise shaping Pixel Colored noise Layout Acoustic noise Trajectory

来源：评论

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Geometric Sequence (GS) Imaging with Bayesian Smoothing for Optical and Capacitive Imaging Sensors

Geometric Sequence (GS) Imaging with Bayesian Smoothing for ...

引用

ieee computer society conference on computer vision and pattern recognition workshops

作者： Kuntal Sengupta Fatih Porikli MERL 201 Broadway Cambridge

ISBN: (纸本)9781424439942

In this paper, we introduce a novel technique called Geometric Sequence (GS) imaging, specifically for the purpose of low power and light weight tracking in human computer interface design. The imaging sensor is programmed to capture the scene with a train of packets, where each packet constitutes a few images. The delay or the baseline associated with consecutive image pairs in a packet follows a fixed ratio, as in a geometric sequence. The image pair with shorter baseline or delay captures fast motion, while the image pair with larger baseline or delay captures slow motion. Given an image packet, the motion confidence maps computed from the slow and the fast image pairs are fused into a single map. Next, we use a Bayesian update scheme to compute the motion hypotheses probability map, given the information of prior packets. We estimate the motion from this probability map. The GS imaging system reliably tracks slow movements as well as fast movements, a feature that is important in realizing applications such as a touchpad type system. Compared to continuous imaging with short delay between consecutive pairs, the GS imaging technique enjoys several advantages. The overall power consumption and the CPU load are significantly low. We present results in the domain of optical camera based human computer interface (HCI) applications, as well as for capacitive fingerprint imaging sensor based touch pad systems.

关键词： Optical imaging Optical sensors Bayesian methods Smoothing methods Geometrical optics Capacitive sensors Image sensors Delay Humans computer interfaces

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Mobile Exergames - Burn Calories While Playing Games on a Smartphone

Mobile Exergames - Burn Calories While Playing Games on a Sm...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Pradeep Buddharaju Naga Siva Chandra Prasad Pamidi Department of Computer Science University of Houston Clear Lake Houston TX USA

Exergames combine exercising with game play by requiring the users to perform some kind of physical activity (and exercise) in order to score points in the game. In this paper, we present a novel mobile exergaming framework, which requires the users to physically move and jump in order to score points in a game that is played on a smartphone. Our system uses a custom designed Exercising Pad (called ExerPad) in order to track the user's physical movement, and then automatically updates the corresponding game character's position on the screen. The ExerPad contains different shaped images, which are captured from the smartphone's inbuilt camera, and are automatically detected by our shape detection algorithm. We also use the smartphone's inbuilt accelerometer and gyroscope to detect other physical movements from the user such as jumping, turning etc. The experimental results show that the proposed mobile exergames helps its users to burn calories and have fun at the same time.

关键词： Games Mobile communication Gyroscopes Accelerometers Cameras Shape conferences

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MSR-VTT: A large video description dataset for bridging video and language

MSR-VTT: A large video description dataset for bridging vide...

引用

2016 ieee conference on computer vision and pattern recognition, CVPR 2016

作者： Xu, Jun Mei, Tao Yao, Ting Rui, Yong Microsoft Research Beijing China

ISBN: (纸本)9781467388511

While there has been increasing interest in the task of describing video with natural language, current computer vision algorithms are still severely limited in terms of the variability and complexity of the videos and their associated language that they can recognize. This is in part due to the simplicity of current benchmarks, which mostly focus on specific fine-grained domains with limited videos and simple descriptions. While researchers have provided several benchmark datasets for image captioning, we are not aware of any large-scale video description dataset with comprehensive categories yet diverse video content. In this paper we present MSR-VTT (standing for "MSR-Video to Text") which is a new large-scale video benchmark for video understanding, especially the emerging task of translating video to text. This is achieved by collecting 257 popular queries from a commercial video search engine, with 118 videos for each query. In its current version, MSR-VTT provides 10K web video clips with 41.2 hours and 200K clip-sentence pairs in total, covering the most comprehensive categories and diverse visual content, and representing the largest dataset in terms of sentence and vocabulary. Each clip is annotated with about 20 natural sentences by 1,327 AMT workers. We present a detailed analysis of MSR-VTT in comparison to a complete set of existing datasets, together with a summarization of different state-of-the-art video-to-text approaches. We also provide an extensive evaluation of these approaches on this dataset, showing that the hybrid Recurrent Neural Networkbased approach, which combines single-frame and motion representations with soft-attention pooling strategy, yields the best generalization capability on MSR-VTT.

关键词： computer vision

来源：评论

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Self-adaptive matrix completion for heart rate estimation from face videos under realistic conditions

Self-adaptive matrix completion for heart rate estimation fr...

引用

2016 ieee conference on computer vision and pattern recognition, CVPR 2016

作者： Tulyakov, Sergey Alameda-Pineda, Xavier Ricci, Elisa Yin, Lijun Cohn, Jeffrey F. Sebe, Nicu University of Trento Via Sommarive 9 Trento38123 Italy Fondazione Bruno Kessler Via Sommarive 18 Trento38123 Italy University of Perugia Via Duranti 93 Perugia06123 Italy State University of New York at Binghamton BinghamtonNY13902 United States Robotics Institute Carnegie Mellon University PittsburghPA15213 United States Department of Psychology University of Pittsburgh PittsburghPA15260 United States

ISBN: (纸本)9781467388511

Recent studies in computer vision have shown that, while practically invisible to a human observer, skin color changes due to blood flow can be captured on face videos and, surprisingly, be used to estimate the heart rate (HR). While considerable progress has been made in the last few years, still many issues remain open. In particular, stateof-the-art approaches are not robust enough to operate in natural conditions (e.g. in case of spontaneous movements, facial expressions, or illumination changes). Opposite to previous approaches that estimate the HR by processing all the skin pixels inside a fixed region of interest, we introduce a strategy to dynamically select face regions useful for robust HR estimation. Our approach, inspired by recent advances on matrix completion theory, allows us to predict the HR while simultaneously discover the best regions of the face to be used for estimation. Thorough experimental evaluation conducted on public benchmarks suggests that the proposed approach significantly outperforms state-ofthe-art HR estimation methods in naturalistic conditions.

关键词： computer vision

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Where are they looking in the 3D space?

Where are they looking in the 3D space?

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Nora Horanyi Linfang Zheng Eunji Chong Aleš Leonardis Hyung Jin Chang School of Computer Science University of Birmingham *** Inc.

We propose a novel depth-aware joint attention target estimation framework that estimates the attention target in 3D space. Our goal is to mimic human’s ability to understand where each person is looking in their proximity. In this work, we tackle the previously unexplored problem of utilising a depth prior along with a 3D joint FOV probability map to estimate the joint attention target of people in the scene. We leverage the insight that besides the 2D image content, strong gaze-related constraints exist in the depth order of the scene and different subject-specific attributes. Extensive experiments show that our method outperforms favourably against existing joint attention target estimation methods on the VideoCoAtt benchmark dataset. Despite the proposed framework being designed for joint attention target estimation, we show that it outperforms single attention target estimation methods on both the GazeFollow image and the VideoAttentionTarget video benchmark datasets.

关键词：

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EFE: End-to-end Frame-to-Gaze Estimation

EFE: End-to-end Frame-to-Gaze Estimation

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Haldun Balim Seonwook Park Xi Wang Xucong Zhang Otmar Hilliges Department of Computer Science ETH Zürich Lunit Inc. Computer Vision Lab Delft University of Technology

Despite the recent development of learning-based gaze estimation methods, most methods require one or more eye or face region crops as inputs and produce a gaze direction vector as output. Cropping results in a higher resolution in the eye regions and having fewer confounding factors (such as clothing and hair) is believed to benefit the final model performance. However, this eye/face patch cropping process is expensive, erroneous, and implementation-specific for different methods. In this paper, we propose a frame-to-gaze network that directly predicts both 3D gaze origin and 3D gaze direction from the raw frame out of the camera without any face or eye cropping. Our method demonstrates that direct gaze regression from the raw downscaled frame, from FHD/HD to VGA/HVGA resolution, is possible despite the challenges of having very few pixels in the eye region. The proposed method achieves comparable results to state-of-the-art methods in Point-of-Gaze (PoG) estimation on three public gaze datasets: GazeCapture, MPIIFaceGaze, and EVE, and generalizes well to extreme camera view changes.

关键词：

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Distributed segmentation and classification of human actions using a wearable motion sensor network

Distributed segmentation and classification of human actions...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： A.Y. Yang S. Iyengar S. Sastry R. Bajcsy P. Kuryloski R. Jafari Department of EECS University of California Berkeley USA Department of ECE Cornell University USA Department of EE University of Technology Dallas USA

We propose a distributed recognition method to classify human actions using a low-bandwidth wearable motion sensor network. Given a set of pre-segmented motion sequences as training examples, the algorithm simultaneously segments and classifies human actions, and it also rejects outlying actions that are not in the training set. The classification is distributedly operated on individual sensor nodes and a base station computer. We show that the distribution of multiple action classes satisfies a mixture subspace model, one sub-space for each action class. Given a new test sample, we seek the sparsest linear representation of the sample w.r.t. all training examples. We show that the dominant coefficients in the representation only correspond to the action class of the test sample, and hence its membership is encoded in the representation. We further provide fast linear solvers to compute such representation via l 1 -minimization. Using up to eight body sensors, the algorithm achieves state-of-the-art 98.8% accuracy on a set of 12 action categories. We further demonstrate that the recognition precision only decreases gracefully using smaller subsets of sensors, which validates the robustness of the distributed framework.

关键词： Humans Wearable sensors Sensor systems Base stations Accelerometers Wireless sensor networks computer networks Biomedical monitoring Distributed computing Testing

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