Efficient activity detection with max-subgraph search

We propose an efficient approach that unifies activity cat-egorization with space-time localization. The main idea is to pose activity detection as a maximum-weight connected subgraph problem over a learned space-time graph con-structed on the test sequence. We show this permits an efficient branch-and-cut solution for the best-scoring—and possibly non-cubically shaped—portion of the video for a given activity classifier. The upshot is a fast method that can evaluate a broader space of candidates than was previ-ously practical, which we find often leads to more accurate detection. We demonstrate the proposed algorithm on three datasets, and show its speed and accuracy advantages over multiple existing search strategies. 1

Learning human activities and poses with interconnected data sources

Understanding human actions and poses in images or videos is a challenging problem in computer vision. There are different topics related to this problem such as action recognition, pose estimation, human-object interaction, and activity detection. Knowledge of actions and poses could benefit many applications, including video search, surveillance, auto-tagging, event detection, and human-computer interfaces. To understand humans' actions and poses, we need to address several challenges. First, humans are able to perform an enormous amount of poses. For example, simply to move forward, we can do crawling, walking, running, and sprinting. These poses all look different and require examples to cover these variations. Second, the appearance of a person's pose changes when looking from different viewing angles. The learned action model needs to cover the variations from different views. Third, many actions involve interactions between people and other objects, so we need to consider the appearance change corresponding to that object as well. Fourth, collecting such data for learning is difficult and expensive. Last, even if we can learn a good model for an action, to localize when and where the action happens in a long video remains a difficult problem due to the large search space. My key idea to alleviate these obstacles in learning humans' actions and poses is to discover the underlying patterns that connect the information from different data sources. Why will there be underlying patterns? The intuition is that all people share the same articulated physical structure. Though we can change our pose, there are common regulations that limit how our pose can be and how it can move over time. Therefore, all types of human data will follow these rules and they can serve as prior knowledge or regularization in our learning framework. If we can exploit these tendencies, we are able to extract additional information from data and use them to improve learning of humans' actions and poses. In particular, we are able to find patterns for how our pose could vary over time, how our appearance looks in a specific view, how our pose is when we are interacting with objects with certain properties, and how part of our body configuration is shared across different poses. If we could learn these patterns, they can be used to interconnect and extrapolate the knowledge between different data sources. To this end, I propose several new ways to connect human activity data. First, I show how to connect snapshot images and videos by exploring the patterns of how our pose could change over time. Building on this idea, I explore how to connect humans' poses across multiple views by discovering the correlations between different poses and the latent factors that affect the viewpoint variations. In addition, I consider if there are also patterns connecting our poses and nearby objects when we are interacting with them. Furthermore, I explore how we can utilize the predicted interaction as a cue to better address existing recognition problems including image re-targeting and image description generation. Finally, after learning models effectively incorporating these patterns, I propose a robust approach to efficiently localize when and where a complex action happens in a video sequence. The variants of my proposed approaches offer a good trade-off between computational cost and detection accuracy. My thesis exploits various types of underlying patterns in human data. The discovered structure is used to enhance the understanding of humans' actions and poses. By my proposed methods, we are able to 1) learn an action with very few snapshots by connecting them to a pool of label-free videos, 2) infer the pose for some views even without any examples by connecting the latent factors between different views, 3) predict the location of an object that a person is interacting with independent of the type and appearance of that object, then use the inferred interaction as a cue to improve recognition, and 4) localize an action in a complex long video. These approaches improve existing frameworks for understanding humans' actions and poses without extra data collection cost and broaden the problems that we can tackle.Computer Science

Automatic Action Annotation in Weakly Labeled Videos

Manual spatio-temporal annotation of human action in videos is laborious, requires several annotators and contains human biases. In this paper, we present a weakly supervised approach to automatically obtain spatio-temporal annotations of an actor in action videos. We first obtain a large number of action proposals in each video. To capture a few most representative action proposals in each video and evade processing thousands of them, we rank them using optical flow and saliency in a 3D-MRF based framework and select a few proposals using MAP based proposal subset selection method. We demonstrate that this ranking preserves the high quality action proposals. Several such proposals are generated for each video of the same action. Our next challenge is to iteratively select one proposal from each video so that all proposals are globally consistent. We formulate this as Generalized Maximum Clique Graph problem using shape, global and fine grained similarity of proposals across the videos. The output of our method is the most action representative proposals from each video. Our method can also annotate multiple instances of the same action in a video. We have validated our approach on three challenging action datasets: UCF Sport, sub-JHMDB and THUMOS'13 and have obtained promising results compared to several baseline methods. Moreover, on UCF Sports, we demonstrate that action classifiers trained on these automatically obtained spatio-temporal annotations have comparable performance to the classifiers trained on ground truth annotation

A random forest approach to segmenting and classifying gestures

This thesis investigates a gesture segmentation and recognition scheme that employs a random forest classification model. A complete gesture recognition system should localize and classify each gesture from a given gesture vocabulary, within a continuous video stream. Thus, the system must determine the start and end points of each gesture in time, as well as accurately recognize the class label of each gesture. We propose a unified approach that performs the tasks of temporal segmentation and classification simultaneously. Our method trains a random forest classification 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. We conclude with a discussion of the advantages of using our model

Large-scale interactive exploratory visual search

Large scale visual search has been one of the challenging issues in the era of big data. It demands techniques that are not only highly effective and efficient but also allow users conveniently express their information needs and refine their intents. In this thesis, we focus on developing an exploratory framework for large scale visual search. We also develop a number of enabling techniques in this thesis, including compact visual content representation for scalable search, near duplicate video shot detection, and action based event detection. We propose a novel scheme for extremely low bit rate visual search, which sends compressed visual words consisting of vocabulary tree histogram and descriptor orientations rather than descriptors. Compact representation of video data is achieved through identifying keyframes of a video which can also help users comprehend visual content efficiently. We propose a novel Bag-of-Importance model for static video summarization. Near duplicate detection is one of the key issues for large scale visual search, since there exist a large number nearly identical images and videos. We propose an improved near-duplicate video shot detection approach for more effective shot representation. Event detection has been one of the solutions for bridging the semantic gap in visual search. We particular focus on human action centred event detection. We propose an enhanced sparse coding scheme to model human actions. Our proposed approach is able to significantly reduce computational cost while achieving recognition accuracy highly comparable to the state-of-the-art methods. At last, we propose an integrated solution for addressing the prime challenges raised from large-scale interactive visual search. The proposed system is also one of the first attempts for exploratory visual search. It provides users more robust results to satisfy their exploring experiences

Multi-Modality Human Action Recognition

Human action recognition is very useful in many applications in various areas, e.g. video surveillance, HCI (Human computer interaction), video retrieval, gaming and security. Recently, human action recognition becomes an active research topic in computer vision and pattern recognition. A number of action recognition approaches have been proposed. However, most of the approaches are designed on the RGB images sequences, where the action data was collected by RGB/intensity camera. Thus the recognition performance is usually related to various occlusion, background, and lighting conditions of the image sequences. If more information can be provided along with the image sequences, more data sources other than the RGB video can be utilized, human actions could be better represented and recognized by the designed computer vision system.;In this dissertation, the multi-modality human action recognition is studied. On one hand, we introduce the study of multi-spectral action recognition, which involves the information from different spectrum beyond visible, e.g. infrared and near infrared. Action recognition in individual spectra is explored and new methods are proposed. Then the cross-spectral action recognition is also investigated and novel approaches are proposed in our work. On the other hand, since the depth imaging technology has made a significant progress recently, where depth information can be captured simultaneously with the RGB videos. The depth-based human action recognition is also investigated. I first propose a method combining different type of depth data to recognize human actions. Then a thorough evaluation is conducted on spatiotemporal interest point (STIP) based features for depth-based action recognition. Finally, I advocate the study of fusing different features for depth-based action analysis. Moreover, human depression recognition is studied by combining facial appearance model as well as facial dynamic model