The Dense Trajectories concept is one of the most successful approaches in action recognition, suitable for scenarios involving a significant amount of motion. However, due to noise and background motion, many generated trajectories are irrelevant to the actual human activity and can potentially lead to performance degradation. In this paper, we propose Localized Trajectories as an improved version of Dense Trajectories where motion trajectories are clustered around human body joints provided by RGB-D cameras and then encoded by local Bag-of-Words. As a result, the Localized Trajectories concept provides a more discriminative representation of actions as compared to Dense Trajectories. Moreover, we generalize Localized Trajectories to 3D by using the modalities offered by RGB-D cameras. One of the main advantages of using RGB-D data to generate trajectories is that they include radial displacements that are perpendicular to the image plane. Extensive experiments and analysis are carried out on five different datasets.
To further improve the accuracy of recognition, @cite_12 proposed to use deep learned features instead of heuristic spatio-temporal local ones such as Trajectory-Shape Descriptor (TSD) @cite_58 , Histogram of Oriented Gradients (HOG) @cite_46 , Histogram of Optical Flow (HOF) @cite_33 , and Motion Boundary Histogram (MBH) @cite_58 .
@cite_46 @cite_58 @cite_33 @cite_12
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This paper introduces a state-of-the-art video representation and applies it to efficient action recognition and detection. We first propose to improve the popular dense trajectory features by explicit camera motion estimation. More specifically, we extract feature point matches between frames using SURF descriptors and dense optical flow. The matches are used to estimate a homography with RANSAC. To improve the robustness of homography estimation, a human detector is employed to remove outlier matches from the human body as human motion is not constrained by the camera. Trajectories consistent with the homography are considered as due to camera motion, and thus removed. We also use the homography to cancel out camera motion from the optical flow. This results in significant improvement on motion-based HOF and MBH descriptors. We further explore the recent Fisher vector as an alternative feature encoding approach to the standard bag-of-words (BOW) histogram, and consider different ways to include spatial layout information in these encodings. We present a large and varied set of evaluations, considering (i) classification of short basic actions on six datasets, (ii) localization of such actions in feature-length movies, and (iii) large-scale recognition of complex events. We find that our improved trajectory features significantly outperform previous dense trajectories, and that Fisher vectors are superior to BOW encodings for video recognition tasks. In all three tasks, we show substantial improvements over the state-of-the-art results. System theoretic approaches to action recognition model the dynamics of a scene with linear dynamical systems (LDSs) and perform classification using metrics on the space of LDSs, e.g. Binet-Cauchy kernels. However, such approaches are only applicable to time series data living in a Euclidean space, e.g. joint trajectories extracted from motion capture data or feature point trajectories extracted from video. Much of the success of recent object recognition techniques relies on the use of more complex feature descriptors, such as SIFT descriptors or HOG descriptors, which are essentially histograms. Since histograms live in a non-Euclidean space, we can no longer model their temporal evolution with LDSs, nor can we classify them using a metric for LDSs. In this paper, we propose to represent each frame of a video using a histogram of oriented optical flow (HOOF) and to recognize human actions by classifying HOOF time-series. For this purpose, we propose a generalization of the Binet-Cauchy kernels to nonlinear dynamical systems (NLDS) whose output lives in a non-Euclidean space, e.g. the space of histograms. This can be achieved by using kernels defined on the original non-Euclidean space, leading to a well-defined metric for NLDSs. We use these kernels for the classification of actions in video sequences using (HOOF) as the output of the NLDS. We evaluate our approach to recognition of human actions in several scenarios and achieve encouraging results. Human action recognition from realistic videos plays a key role in multimedia event detection and understanding. In this paper, a novel Trajectory Based Covariance (TBC) descriptor is proposed, which is formulated along the dense trajectories. To map the descriptor matrix to vector space and trim out the redundancy of data, the TBC descriptor matrix is projected to Euclidean space by the Logarithm Principal Components Analysis (LogPCA). Our method is tested on the challenging Hollywood2 and TV Human Interaction datasets. Experimental results show that the proposed TBC descriptor outperforms three baseline descriptors (i.e., histogram of oriented gradient, histogram of optical flow and motion boundary histogram), and our method achieves better recognition performances than a number of state-of-the-art approaches. The purpose of this paper is to describe one-shot-learning gesture recognition systems developed on the ChaLearn Gesture Dataset (ChaLearn). We use RGB and depth images and combine appearance (Histograms of Oriented Gradients) and motion descriptors (Histogram of Optical Flow) for parallel temporal segmentation and recognition. The Quadratic-Chi distance family is used to measure differences between histograms to capture cross-bin relationships. We also propose a new algorithm for trimming videos--to remove all the unimportant frames from videos. We present two methods that use a combination of HOG-HOF descriptors together with variants of a Dynamic Time Warping technique. Both methods outperform other published methods and help narrow the gap between human performance and algorithms on this task. The code is publicly available in the MLOSS repository. Suitable feature representation is essential for performing video analysis and understanding in applications within the smart surveillance domain. In this paper, we propose a novel spatiotemporal feature descriptor based on co-occurrence matrices computed from the optical flow magnitude and orientation. Our method, called Optical Flow Co-occurrence Matrices (OFCM), extracts a robust set of measures known as Haralick features to describe the flow patterns by measuring meaningful properties such as contrast, entropy and homogeneity of co-occurrence matrices to capture local space-time characteristics of the motion through the neighboring optical flow magnitude and orientation. We evaluate the proposed method on the action recognition problem by applying a visual recognition pipeline involving bag of local spatiotemporal features and SVM classification. The experimental results, carried on three well-known datasets (KTH, UCF Sports and HMDB51), demonstrate that OFCM outperforms the results achieved by several widely employed spatiotemporal feature descriptors such as HOF, HOG3D and MBH, indicating its suitability to be used as video representation. This paper introduces a high efficient local spatiotemporal descriptor, called gradient boundary histograms (GBH). The proposed GBH descriptor is built on simple spatio-temporal gradients, which are fast to compute. We demonstrate that it can better represent local structure and motion than other gradient-based descriptors, and significantly outperforms them on large realistic datasets. A comprehensive evaluation shows that the recognition accuracy is preserved while the spatial resolution is greatly reduced, which yields both high efficiency and low memory usage. We propose an algorithm which combines the discriminative information from depth images as well as from 3D joint positions to achieve high action recognition accuracy. To avoid the suppression of subtle discriminative information and also to handle local occlusions, we compute a vector of many independent local features. Each feature encodes spatiotemporal variations of depth and depth gradients at a specific space-time location in the action volume. Moreover, we encode the dominant skeleton movements by computing a local 3D joint position difference histogram. For each joint, we compute a 3D space-time motion volume which we use as an importance indicator and incorporate in the feature vector for improved action discrimination. To retain only the discriminant features, we train a random decision forest (RDF). The proposed algorithm is evaluated on three standard datasets and compared with nine state-of-the-art algorithms. Experimental results show that, on the average, the proposed algorithm outperform all other algorithms in accuracy and have a processing speed of over 112 frames second. This paper presents an approach for human action recognition by finding the discriminative key frames from a video sequence and representing them with the distribution of local motion features and their spatiotemporal arrangements. In this approach, the key frames of the video sequence are selected by their discriminative power and represented by the local motion features detected in them and integrated from their temporal neighbors. In the key frame’s representation, the spatial arrangements of the motion features are captured in a hierarchical spatial pyramid structure. By using frame by frame voting for the recognition, experiments have demonstrated improved performances over most of the other known methods on the popular benchmark data sets. Recognizinghumanactionfromimagesequencesis an appealingyet challengingproblem in computer vision with many applications including motion capture, human-computer interaction, environment control, and security surveillance. In this paper, we focus on recognizing the activities of a person in an image sequence from local motion features and their spatiotemporal arrangements. Our approach is motivated by the recent success of “bag-of-words” model for general object recognition in computer vision[21, 14]. This representation, which is adapted from the text retrieval literature, models the object by the distribution of words from a fixed visual code book, which is usually obtained by vector quantization of local image visual features. However, this method discards the spatial and the temporal relations among these visual features, which could be helpful in object recognition. Addressing this problem, our approach uses a hierarchical representation for the key frames of a given video sequence to integrate information from both the spatial and the temporal domains. We first apply a spatiotemporal feature detector to the video sequence and obtain the local motion features. Then we generate a visual word code book by quantization of the local motion features and assign a word label to each of them. Next we select key frames of the video sequence by their discriminative power. Then, for each key frame, we integrate the visual words from its nearby frames, divide the key frame spatially into finer subdivisions and compute in each cell the histograms of the visual words detected in this key frame and its temporal neighbors. Finally, we concatenate the histograms from all cells and use This paper introduces a video representation based on dense trajectories and motion boundary descriptors. Trajectories capture the local motion information of the video. A dense representation guarantees a good coverage of foreground motion as well as of the surrounding context. A state-of-the-art optical flow algorithm enables a robust and efficient extraction of dense trajectories. As descriptors we extract features aligned with the trajectories to characterize shape (point coordinates), appearance (histograms of oriented gradients) and motion (histograms of optical flow). Additionally, we introduce a descriptor based on motion boundary histograms (MBH) which rely on differential optical flow. The MBH descriptor shows to consistently outperform other state-of-the-art descriptors, in particular on real-world videos that contain a significant amount of camera motion. We evaluate our video representation in the context of action classification on nine datasets, namely KTH, YouTube, Hollywood2, UCF sports, IXMAS, UIUC, Olympic Sports, UCF50 and HMDB51. On all datasets our approach outperforms current state-of-the-art results. This paper presents a new framework for human activity recognition from video sequences captured by a depth camera. We cluster hypersurface normals in a depth sequence to form the polynormal which is used to jointly characterize the local motion and shape information. In order to globally capture the spatial and temporal orders, an adaptive spatio-temporal pyramid is introduced to subdivide a depth video into a set of space-time grids. We then propose a novel scheme of aggregating the low-level polynormals into the super normal vector (SNV) which can be seen as a simplified version of the Fisher kernel representation. In the extensive experiments, we achieve classification results superior to all previous published results on the four public benchmark datasets, i.e., MSRAction3D, MSRDailyActivity3D, MSRGesture3D, and MSRActionPairs3D. We propose a feature-based 2D+3D multimodal facial expression recognition method.It is fully automatic benefit from a large set of automatically detected landmarks.The complementarities between 2D and 3D features are comprehensively demonstrated.Our method achieves the best accuracy on the BU-3DFE database so far.A good generalization ability is shown on the Bosphorus database. We present a fully automatic multimodal 2D + 3D feature-based facial expression recognition approach and demonstrate its performance on the BU-3DFE database. Our approach combines multi-order gradient-based local texture and shape descriptors in order to achieve efficiency and robustness. First, a large set of fiducial facial landmarks of 2D face images along with their 3D face scans are localized using a novel algorithm namely incremental Parallel Cascade of Linear Regression (iPar-CLR). Then, a novel Histogram of Second Order Gradients (HSOG) based local image descriptor in conjunction with the widely used first-order gradient based SIFT descriptor are used to describe the local texture around each 2D landmark. Similarly, the local geometry around each 3D landmark is described by two novel local shape descriptors constructed using the first-order and the second-order surface differential geometry quantities, i.e., Histogram of mesh Gradients (meshHOG) and Histogram of mesh Shape index (curvature quantization, meshHOS). Finally, the Support Vector Machine (SVM) based recognition results of all 2D and 3D descriptors are fused at both feature-level and score-level to further improve the accuracy. Comprehensive experimental results demonstrate that there exist impressive complementary characteristics between the 2D and 3D descriptors. We use the BU-3DFE benchmark to compare our approach to the state-of-the-art ones. Our multimodal feature-based approach outperforms the others by achieving an average recognition accuracy of 86.32 . Moreover, a good generalization ability is shown on the Bosphorus database. This paper presents a novel approach to face recognition, named Multi-resolution Histograms of Local Variation Patterns (MHLVP), in which face images are represented as the concatenation of the local spatial histogram of local variation patterns computed from the multi-resolution Gabor features. For a face image with abundant texture and shape information, aGabor feature map(GFM) is computed by convolving the image with each of the forty multi-scale and multi-orientation Gabor filters. Each GFM is then divided into small non-overlapping regions to enhance its shape information, and then Local Binary Pattern (LBP) histograms are extracted for each region and concatenated into a feature histogram to enhance the texture information in the specific GFM. Further more, all of the feature histograms extracted from the forty GFMs are further concatenated into a single feature histogram as the final representation of the given face image. Eventually, the identification is achieved by histogram intersection operation. Our experimental results on FERET face databases show that the proposed method performs terrifically better than the performance of some classical results including the best results in FERET'97. We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds. This paper describes novel event-based spatio-temporal features called time-surfaces and how they can be used to create a hierarchical event-based pattern recognition architecture. Unlike existing hierarchical architectures for pattern recognition, the presented model relies on a time oriented approach to extract spatio-temporal features from the asynchronously acquired dynamics of a visual scene. These dynamics are acquired using biologically inspired frameless asynchronous event-driven vision sensors. Similarly to cortical structures, subsequent layers in our hierarchy extract increasingly abstract features using increasingly large spatio-temporal windows. The central concept is to use the rich temporal information provided by events to create contexts in the form of time-surfaces which represent the recent temporal activity within a local spatial neighborhood. We demonstrate that this concept can robustly be used at all stages of an event-based hierarchical model. First layer feature units operate on groups of pixels, while subsequent layer feature units operate on the output of lower level feature units. We report results on a previously published 36 class character recognition task and a four class canonical dynamic card pip task, achieving near 100 percent accuracy on each. We introduce a new seven class moving face recognition task, achieving 79 percent accuracy. In this paper, we propose a new, compact, 4D spatio-temporal “Nebula” feature to improve expression and facial movement analysis performance. Given a spatio-temporal volume, the data is voxelized and fit to a cubic polynomial. A label is assigned based on the principal curvature values, and the polar angles of the direction of least curvature are computed. The labels and angles for each feature are used to build a histogram for each region of the face. The concatenated histograms from each region give us our final feature vector. This feature description is tested on the posed expression database BU-4DFE and on a new 4D spontaneous expression database. Various region configurations, histogram sizes, and feature parameters are tested, including a non-dynamic version of the approach. The LBP-TOP approach on the texture image as well as on the depth image is also tested for comparison. The onsets of the six canonical expressions are classified for 100 subjects in BU-4DFE, while the onset, offset, and non-existence of 12 Action Units (AUs) are classified for 16 subjects from our new spontaneous database. For posed expression recognition, the Nebula feature approach shows improvement over LBPTOP on the depth images and significant improvement over the non-dynamic 3D-only approach. Moreover, the Nebula feature performs better for AU classification than the compared approaches for 11 of the AUs tested in terms of accuracy as well as Area Under Receiver Operating Characteristic Curve (AUC). Object detection has seen huge progress in recent years, much thanks to the heavily-engineered Histograms of Oriented Gradients (HOG) features. Can we go beyond gradients and do better than HOG? We provide an affirmative answer by proposing and investigating a sparse representation for object detection, Histograms of Sparse Codes (HSC). We compute sparse codes with dictionaries learned from data using K-SVD, and aggregate per-pixel sparse codes to form local histograms. We intentionally keep true to the sliding window framework (with mixtures and parts) and only change the underlying features. To keep training (and testing) efficient, we apply dimension reduction by computing SVD on learned models, and adopt supervised training where latent positions of roots and parts are given externally e.g. from a HOG-based detector. By learning and using local representations that are much more expressive than gradients, we demonstrate large improvements over the state of the art on the PASCAL benchmark for both root-only and part-based models. We present a new descriptor for activity recognition from videos acquired by a depth sensor. Previous descriptors mostly compute shape and motion features independently, thus, they often fail to capture the complex joint shape-motion cues at pixel-level. In contrast, we describe the depth sequence using a histogram capturing the distribution of the surface normal orientation in the 4D space of time, depth, and spatial coordinates. To build the histogram, we create 4D projectors, which quantize the 4D space and represent the possible directions for the 4D normal. We initialize the projectors using the vertices of a regular polychoron. Consequently, we refine the projectors using a discriminative density measure, such that additional projectors are induced in the directions where the 4D normals are more dense and discriminative. Through extensive experiments, we demonstrate that our descriptor better captures the joint shape-motion cues in the depth sequence, and thus outperforms the state-of-the-art on all relevant benchmarks. The goal of the paper is to develop a one-shot real-time learning and recognition system for 3D actions. We use RGBD images, combine motion and appearance cues, and map them into a new overcomplete space. The proposed method relies on descriptors based on 3D Histogram of Flow (3DHOF) and on Global Histogram of Oriented Gradient (GHOG); adaptive sparse coding (SC) is further applied to capture high-level patterns. We add effective on-line video segmentation and finally the recognition of actions through linear SVMs. The main contribution of the paper is a real-time system for one-shot action modeling; moreover we highlight the effectiveness of sparse coding techniques to represent 3D actions. We obtain very good results on the ChaLearn Gesture Dataset and with a Kinect sensor. A spatio-temporal descriptor for representation and recognition of time-varying textures is proposed (binarized statis- tical image features on three orthogonal planes (BSIF-TOP)) in this paper. The descriptor, similar in spirit to the well known local binary patterns on three orthogonal planes approach, estimates histograms of binary coded image sequences on three orthogonal planes corresponding to spatial spatio-temporal dimensions. However, unlike some other methods which generate the code in a heuristic fashion, binary code generation in the BSIF-TOP approach is realized by filtering operations on different regions of spatial spatio-temporal support and by binarizing the filter responses. The filters are learnt via independent component analysis on each of three planes after preprocessing using a whitening transformation. By extending the BSIF-TOP descriptor to a multiresolution scheme, the descriptor is able to capture the spatio-temporal content of an image sequence at multiple scales, improving its representation capacity. In the evaluations on the UCLA, Dyntex, and Dyntex dynamic texture databases, the proposed method achieves very good performance compared to existing approaches. In this paper we present two variant formulations of the well-known Histogram of Oriented Gradients (HOG) features and provide a comparison of these features on a large scale sign detection problem. The aim of this research is to find features capable of driving further improvements atop a preexisting detection framework used commercially to detect traffic signs on the scale of entire national road networks (1000's of kilometres of video). We assume the computationally efficient framework of a cascade of boosted weak classifiers. Rather than comparing features on the general problem of detection we compare their merits in the final stages of a cascaded detection problem where a feature's ability to reduce error is valued more highly than computational efficiency. Results show the benefit of the two new features on a New Zealand speed sign detection problem. We also note the importance of using non-sign training and validation instances taken from the same video data that contains the training and validation positives. This is attributed to the potential for the more powerful HOG features to overfit on specific local patterns which may be present in alternative video data.