In this work, we address a challenging problem of fine-grained and coarse-grained recognition of object manipulation actions. Due to the variations in geometrical and motion constraints, there are different manipulations actions possible to perform different sets of actions with an object. Also, there are subtle movements involved to complete most of object manipulation actions. This makes the task of object manipulation action recognition difficult with only just the motion information. We propose to use grasp and motion-constraints information to recognise and understand action intention with different objects. We also provide an extensive experimental evaluation on the recent Yale Human Grasping dataset consisting of large set of 455 manipulation actions. The evaluation involves a) Different contemporary multi-class classifiers, and binary classifiers with one-vs-one multi- class voting scheme, b) Differential comparisons results based on subsets of attributes involving information of grasp and motion-constraints, c) Fine-grained and Coarse-grained object manipulation action recognition based on fine-grained as well as coarse-grained grasp type information, and d) Comparison between Instance level and Sequence level modeling of object manipulation actions. Our results justifies the efficacy of grasp attributes for the task of fine-grained and coarse-grained object manipulation action recognition.
Hand gesture recognition is more closer to the problem of object manipulation action recognition. Hand gesture recognition has also been addressed using depth data generated from Kinect in @cite_18 and @cite_27 . But these techniques mainly target sign language gestures and not the human hand-object interactions. @cite_27 treats an action sequence as a 4D shape and propose random occupancy pattern (ROP) features, extracted from random sampling of 4D subvolumes with different sizes and at different locations. In gesture depth sequences, the semantics of the gestures are mainly understood by the large movement of the hand. These approaches use cropped portion of hand using some hand detection approach, to determine these large hand movements to model different gestures. But, these clear motion information are not easily perceivable in the case object manipulation tasks.
@cite_27 @cite_18
2169251375 2119656522 2008110769 1966086309 2595328592 2145835757 2125201599 2094645604 2219948256 2791833757 1561406421 1971252500 2112480082 2072028093 2116362072 2769039400 2168312374 2777985171 2015551990 2024210451
We study the problem of action recognition from depth sequences captured by depth cameras, where noise and occlusion are common problems because they are captured with a single commodity camera. In order to deal with these issues, we extract semi-local features called random occupancy pattern ROP features, which employ a novel sampling scheme that effectively explores an extremely large sampling space. We also utilize a sparse coding approach to robustly encode these features. The proposed approach does not require careful parameter tuning. Its training is very fast due to the use of the high-dimensional integral image, and it is robust to the occlusions. Our technique is evaluated on two datasets captured by commodity depth cameras: an action dataset and a hand gesture dataset. Our classification results are superior to those obtained by the state of the art approaches on both datasets. This paper presents a novel and real-time system for interaction with an application or video game via hand gestures. Our system includes detecting and tracking bare hand in cluttered background using skin detection and hand posture contour comparison algorithm after face subtraction, recognizing hand gestures via bag-of-features and multiclass support vector machine (SVM) and building a grammar that generates gesture commands to control an application. In the training stage, after extracting the keypoints for every training image using the scale invariance feature transform (SIFT), a vector quantization technique will map keypoints from every training image into a unified dimensional histogram vector (bag-of-words) after K-means clustering. This histogram is treated as an input vector for a multiclass SVM to build the training classifier. In the testing stage, for every frame captured from a webcam, the hand is detected using our algorithm, then, the keypoints are extracted for every small image that contains the detected hand gesture only and fed into the cluster model to map them into a bag-of-words vector, which is finally fed into the multiclass SVM training classifier to recognize the hand gesture. In this paper we propose a hand gesture recognition system that relies on color and depth images, and on a small pose sensor on the human palm. Monocular and stereo vision systems have been used for human pose and gesture recognition, but with limited scope due to limitations on texture, illumination, etc. New RGB-Depth sensors, that reply on projected light such as the Microsoft Kinect, have overcome many of those limitations. However, the point clouds for hand gestures are still in many cases noisy and partially occluded, and hand gesture recognition is not trivial. Hand gesture recognition is much more complex than full body motion, since we can have the hands in any orientation and can not assume a standing body on a ground plane. In this work we propose to add a tiny pose sensor to the human palm, with a minute accelerometer and magnetometer that combined provide 3D angular pose, to reduce the search space and have a robust and computationally light recognition method. Starting with the full depth image point cloud, segmentation can be performed by taking into account the relative depth and hand orientation, as well as skin color. Identification is then performed by matching 3D voxel occupancy against a gesture template database. Preliminary results are presented for the recognition of Portuguese Sign Language alphabet, showing the validity of the approach. In this paper, we propose a real-time hand gesture recognition system using a thinning method utilizing a stereo camera. We implement a depth map in the hand detection portion that uses a sum of absolute differences method based on the acquired right-left image to detect the foreground object. We use a convex hull to detect the region of interests (ROI) and calculate the depth of object in ROI to obtain hand images that are more accurate. Then, we remove the background image in ROI and get the foreground image as a hand image. Finally, we use a blob labeling method to obtain the clean hand image, without the noise caused by computing distance from stereo matching. The hand gesture recognition system uses the Zhang and Suen thinning algorithm to obtain the feature point, angle and distance. It recognizes five kinds of hand gestures. The proposed method achieves an average recognition rate of 82.93 . Gesture recognition aims to recognize meaningful movements of human bodies, and is of utmost importance in intelligent human–computer robot interactions. In this paper, we present a multimodal gesture recognition method based on 3-D convolution and convolutional long-short-term-memory (LSTM) networks. The proposed method first learns short-term spatiotemporal features of gestures through the 3-D convolutional neural network, and then learns long-term spatiotemporal features by convolutional LSTM networks based on the extracted short-term spatiotemporal features. In addition, fine-tuning among multimodal data is evaluated, and we find that it can be considered as an optional skill to prevent overfitting when no pre-trained models exist. The proposed method is verified on the ChaLearn LAP large-scale isolated gesture data set (IsoGD) and the Sheffield Kinect gesture (SKIG) data set. The results show that our proposed method can obtain the state-of-the-art recognition accuracy (51.02 on the validation set of IsoGD and 98.89 on SKIG). We introduce a discriminative hidden-state approach for the recognition of human gestures. Gesture sequences often have a complex underlying structure, and models that can incorporate hidden structures have proven to be advantageous for recognition tasks. Most existing approaches to gesture recognition with hidden states employ a Hidden Markov Model or suitable variant (e.g., a factored or coupled state model) to model gesture streams; a significant limitation of these models is the requirement of conditional independence of observations. In addition, hidden states in a generative model are selected to maximize the likelihood of generating all the examples of a given gesture class, which is not necessarily optimal for discriminating the gesture class against other gestures. Previous discriminative approaches to gesture sequence recognition have shown promising results, but have not incorporated hidden states nor addressed the problem of predicting the label of an entire sequence. In this paper, we derive a discriminative sequence model with a hidden state structure, and demonstrate its utility both in a detection and in a multi-way classification formulation. We evaluate our method on the task of recognizing human arm and head gestures, and compare the performance of our method to both generative hidden state and discriminative fully-observable models. Within the context of hand gesture recognition, spatiotemporal gesture segmentation is the task of determining, in a video sequence, where the gesturing hand is located and when the gesture starts and ends. Existing gesture recognition methods typically assume either known spatial segmentation or known temporal segmentation, or both. This paper introduces a unified framework for simultaneously performing spatial segmentation, temporal segmentation, and recognition. In the proposed framework, information flows both bottom-up and top-down. A gesture can be recognized even when the hand location is highly ambiguous and when information about when the gesture begins and ends is unavailable. Thus, the method can be applied to continuous image streams where gestures are performed in front of moving, cluttered backgrounds. The proposed method consists of three novel contributions: a spatiotemporal matching algorithm that can accommodate multiple candidate hand detections in every frame, a classifier-based pruning framework that enables accurate and early rejection of poor matches to gesture models, and a subgesture reasoning algorithm that learns which gesture models can falsely match parts of other longer gestures. The performance of the approach is evaluated on two challenging applications: recognition of hand-signed digits gestured by users wearing short-sleeved shirts, in front of a cluttered background, and retrieval of occurrences of signs of interest in a video database containing continuous, unsegmented signing in American sign language (ASL). This paper presents a literature review on the use of depth for hand tracking and gesture recognition. The survey examines 37 papers describing depth-based gesture recognition systems in terms of (1) the hand localization and gesture classification methods developed and used, (2) the applications where gesture recognition has been tested, and (3) the effects of the low-cost Kinect and OpenNI software libraries on gesture recognition research. The survey is organized around a novel model of the hand gesture recognition process. In the reviewed literature, 13 methods were found for hand localization and 11 were found for gesture classification. 24 of the papers included real-world applications to test a gesture recognition system, but only 8 application categories were found (and three applications accounted for 18 of the papers). The papers that use the Kinect and the OpenNI libraries for hand tracking tend to focus more on applications than on localization and classification methods, and show that the OpenNI hand tracking method is good enough for the applications tested thus far. However, the limitations of the Kinect and other depth sensors for gesture recognition have yet to be tested in challenging applications and environments. This paper discusses the development of a natural gesture user interface that tracks and recognizes in real time hand gestures based on depth data collected by a Kinect sensor. The interest space corresponding to the hands is first segmented based on the assumption that the hand of the user is the closest object in the scene to the camera. A novel algorithm is proposed to improve the scanning time in order to identify the first pixel on the hand contour within this space. Starting from this pixel, a directional search algorithm allows for the identification of the entire hand contour. The @math -curvature algorithm is then employed to locate the fingertips over the contour, and dynamic time warping is used to select gesture candidates and also to recognize gestures by comparing an observed gesture with a series of prerecorded reference gestures. The comparison of results with state-of-the-art approaches shows that the proposed system outperforms most of the solutions for the static recognition of sign digits and is similar in terms of performance for the static and dynamic recognition of popular signs and for the sign language alphabet. The solution simultaneously deals with static and dynamic gestures as well as with multiple hands within the interest space. An average recognition rate of 92.4 is achieved over 55 static and dynamic gestures. Two possible applications of this work are discussed and evaluated: one for interpretation of sign digits and gestures for a friendlier human-machine interaction and the other one for the natural control of a software interface. A large number of works in egocentric vision have concentrated on action and object recognition. Detection and segmentation of hands in first-person videos, however, has less been explored. For many applications in this domain, it is necessary to accurately segment not only hands of the camera wearer but also the hands of others with whom he is interacting. Here, we take an in-depth look at the hand segmentation problem. In the quest for robust hand segmentation methods, we evaluated the performance of the state of the art semantic segmentation methods, off the shelf and fine-tuned, on existing datasets. We fine-tune RefineNet, a leading semantic segmentation method, for hand segmentation and find that it does much better than the best contenders. Existing hand segmentation datasets are collected in the laboratory settings. To overcome this limitation, we contribute by collecting two new datasets: a) EgoYouTubeHands including egocentric videos containing hands in the wild, and b) HandOverFace to analyze the performance of our models in presence of similar appearance occlusions. We further explore whether conditional random fields can help refine generated hand segmentations. To demonstrate the benefit of accurate hand maps, we train a CNN for hand-based activity recognition and achieve higher accuracy when a CNN was trained using hand maps produced by the fine-tuned RefineNet. Finally, we annotate a subset of the EgoHands dataset for fine-grained action recognition and show that an accuracy of 58.6 can be achieved by just looking at a single hand pose which is much better than the chance level (12.5 ). This work investigates a gesture segmentation and recognition scheme that employs a random forest classification model. Our method trains a random forest model to recognize gestures from a given vocabulary, as presented in a training dataset of video plus 3D body joint locations, as well as out-of-vocabulary (non-gesture) instances. Given an input video stream, our trained model is applied to candidate gestures using sliding windows at multiple temporal scales. The class label with the highest classifier confidence is selected, and its corresponding scale is used to determine the segmentation boundaries in time. We evaluated our formulation in segmenting and recognizing gestures from two different benchmark datasets: the NATOPS dataset of 9,600 gesture instances from a vocabulary of 24 aircraft handling signals, and the ChaLearn dataset of 7,754 gesture instances from a vocabulary of 20 Italian communication gestures. The performance of our method compares favorably with state-of-the-art methods that employ Hidden Markov Models or Hidden Conditional Random Fields on the NATOPS dataset. Gesture recognition remains a very challenging task in the field of computer vision and human computer interaction (HCI). A decade ago the task seemed to be almost unsolvable with the data provided by a single RGB camera. Due to recent advances in sensing technologies, such as time-of-flight and structured light cameras, there are new data sources available, which make hand gesture recognition more feasible. In this work, we propose a highly precise method to recognize static gestures from a depth data, provided from one of the above mentioned devices. The depth images are used to derive rotation-, translation- and scale-invariant features. A multi-layered random forest (MLRF) is then trained to classify the feature vectors, which yields to the recognition of the hand signs. The training time and memory required by MLRF are much smaller, compared to a simple random forest with equivalent precision. This allows to repeat the training procedure of MLRF without significant effort. To show the advantages of our technique, we evaluate our algorithm on synthetic data, on publicly available dataset, containing 24 signs from American Sign Language(ASL) and on a new dataset, collected using recently appeared Intel Creative Gesture Camera. For one-shot learning gesture recognition, two important challenges are: how to extract distinctive features and how to learn a discriminative model from only one training sample per gesture class. For feature extraction, a new spatio-temporal feature representation called 3D enhanced motion scale-invariant feature transform (3D EMoSIFT) is proposed, which fuses RGB-D data. Compared with other features, the new feature set is invariant to scale and rotation, and has more compact and richer visual representations. For learning a discriminative model, all features extracted from training samples are clustered with the k-means algorithm to learn a visual codebook. Then, unlike the traditional bag of feature (BoF) models using vector quantization (VQ) to map each feature into a certain visual codeword, a sparse coding method named simulation orthogonal matching pursuit (SOMP) is applied and thus each feature can be represented by some linear combination of a small number of codewords. Compared with VQ, SOMP leads to a much lower reconstruction error and achieves better performance. The proposed approach has been evaluated on ChaLearn gesture database and the result has been ranked amongst the top best performing techniques on ChaLearn gesture challenge (round 2). Gesture recognition is essential for human — machine interaction. In this paper we propose a method to recognize human gestures using a Kinect® depth camera. The camera views the subject in the front plane and generates a depth image of the subject in the plane towards the camera. This depth image is then used for background removal, followed by generation of the depth profile of the subject. In addition to this, the difference between subsequent frames gives the motion profile of the subject and is used for recognition of gestures. These allow the efficient use of depth camera to successfully recognize multiple human gestures. The result of a case study involving 8 gestures is shown. The system was trained using a multi class Support Vector Machine. This paper implements a real-time hand gesture recognition algorithm based on the inexpensive Kinect sensor. The use of a depth sensor allows for complex 3D gestures where the system is robust to disturbing objects or persons in the background. A Haarlet-based hand gesture recognition system is implemented to detect hand gestures in any orientation, and more in particular pointing gestures while extracting the 3D pointing direction. The system is integrated on an interactive robot (based on ROS), allowing for real-time hand gesture interaction with the robot. Pointing gestures are translated into goals for the robot, telling him where to go. A demo scenario is presented where the robot looks for persons to interact with, asks for directions, and then detects a 3D pointing direction. The robot then explores his vicinity in the given direction and looks for a new person to interact with. Gesture recognition aims at understanding the ongoing human gestures. In this paper, we present a deep architecture to learn spatiotemporal features for gesture recognition. The deep architecture first learns 2D spatiotemporal feature maps using 3D convolutional neural networks (3DCNN) and bidirectional convolutional long-short-term-memory networks (ConvLSTM). The learnt 2D feature maps can encode the global temporal information and local spatial information simultaneously. Then, 2DCNN is utilized further to learn the higher-level spatiotemporal features from the 2D feature maps for the final gesture recognition. The spatiotemporal correlation information is kept through the whole process of feature learning. This makes the deep architecture an effective spatiotemporal feature learner. Experiments on the ChaLearn LAP large-scale isolated gesture dataset (IsoGD) and the Sheffield Kinect Gesture (SKIG) dataset demonstrate the superiority of the proposed deep architecture. Existing gesture segmentations use the backward spotting scheme that first detects the end point, then traces back to the start point and sends the extracted gesture segment to the hidden Markov model (HMM) for gesture recognition. This makes an inevitable time delay between the gesture segmentation and recognition and is not appropriate for continuous gesture recognition. To solve this problem, we propose a forward spotting scheme that executes gesture segmentation and recognition simultaneously. The start and end points of gestures are determined by zero crossing from negative to positive (or from positive to negative) of a competitive differential observation probability that is defined by the difference of observation probability between the maximal gesture and the non-gesture. We also propose the sliding window and accumulative HMMs. The former is used to alleviate the effect of incomplete feature extraction on the observation probability and the latter improves the gesture recognition rate greatly by accepting all accumulated gesture segments between the start and end points and deciding the gesture type by a majority vote of all intermediate recognition results. We use the predetermined association mapping to determine the 3D articulation data, which reduces the feature extraction time greatly. We apply the proposed simultaneous gesture segmentation and recognition method to recognize the upper-body gestures for controlling the curtains and lights in a smart home environment. Experimental results show that the proposed method has a good recognition rate of 95.42 for continuously changing gestures. Abstract In this work, we address human activity and hand gesture recognition problems using 3D data sequences obtained from full-body and hand skeletons, respectively. To this aim, we propose a deep learning-based approach for temporal 3D pose recognition problems based on a combination of a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) recurrent network. We also present a two-stage training strategy which firstly focuses on CNN training and, secondly, adjusts the full method (CNN+LSTM). Experimental testing demonstrated that our training method obtains better results than a single-stage training strategy. Additionally, we propose a data augmentation method that has also been validated experimentally. Finally, we perform an extensive experimental study on publicly available data benchmarks. The results obtained show how the proposed approach reaches state-of-the-art performance when compared to the methods identified in the literature. The best results were obtained for small datasets, where the proposed data augmentation strategy has greater impact. Recently, great progress has been made in sign language recognition. Most approaches are based on the Hidden Markov Model (HMM) with various features, such as motion trajectory. Recognition for sign sentences is obtained from optimal path by Viterbi algorithm, however, some wrong jumps are usually caused by transitional movements between signs. To address the problem, in this paper, we propose an approach consisting of two stages: offline training and online recognition. In the offline training stage, we propose a threshold matrix and rate thresholds. Each element of the threshold matrix describes the minimal probability when a segment belongs to a sign, and rate thresholds are defined as the average probability for signs. So, if certain segment's evaluation is smaller than all the thresholds, it is regarded as a transitional movement and then it should be removed. In the online recognition stage, coarse segmentation, based on the threshold matrix, records the time interval for fine segmentation, and fine segmentation, based on Dynamic Time Warping(DTW) and Length-Root method, determines the endpoint for each candidate sign and selects the most possible one. The final recognition is obtained by concatenating the most possible signs. We evaluate our approach with Kinect-based dataset and the experiments demonstrate the effectiveness of our approach. This paper presents a real-time and robust approach to recognize two types of gestures consisting of seven motional gestures and six finger spelling gestures. This approach utilizes stereo images captured by a stereo webcam to achieve robust recognition under realistic lighting conditions and in various backgrounds. It incorporates several existing computationally efficient techniques and introduces a rule-based approach to merge the information from a pair of stereo images leading to an improved hand detection compared to using single images. The results obtained indicate that high recognition rates under realistic conditions are obtained in real-time on PC platforms at the rate of 30 frames per second. It is shown that its outcome is comparable to two existing approaches while it is computationally more efficient than these approaches.