Imagine a video taken on a sunny beach, can a computer automatically tell what is happening in the scene? Can it identify different human activities in the video, such as water surfing, people walking and lying on the beach? To automatically classify or localize different actions in video sequences is very useful for a variety of tasks, such as video surveillance, objectlevel video summarization, video indexing, digital library organization, etc. However, it remains a challenging task for computers to achieve robust action recognition due to cluttered background, camera motion, occlusion, and geometric and photometric variances of objects. For example, in a live video of a skating competition, the skater moves rapidly across the rink, and the camera also moves to follow the skater. With moving camera, non-stationary background, and moving target, few vision algorithms could identify, categorize and

We address recognition and localization of human actions in realistic scenarios. In contrast to the previous work studying human actions in controlled settings, here we train and test algorithms on real movies with substantial variation of actions in terms of subject appearance, motion, surrounding scenes, viewing angles and spatio-temporal extents. We introduce a new annotated human action dataset and use it to evaluate several existing methods. We in particular focus on boosted space-time window classifiers and introduce “keyframe priming ” that combines discriminative models of human motion and shape within an action. Keyframe priming is shown to significantly improve the performance of action detection. We present detection results for the action class “drinking ” evaluated on two episodes of the movie “Coffee and Cigarettes”. 1.

In this work, we present a novel local descriptor for video sequences. The proposed descriptor is based on histograms of oriented 3D spatio-temporal gradients. Our contribution is four-fold. (i) To compute 3D gradients for arbitrary scales, we develop a memory-efficient algorithm based on integral videos. (ii) We propose a generic 3D orientation quantization which is based on regular polyhedrons. (iii) We perform an in-depth evaluation of all descriptor parameters and optimize them for action recognition. (iv) We apply our descriptor to various action datasets (KTH, Weizmann, Hollywood) and show that we outperform the state-of-the-art. 1

The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially-aware computing.

Abstract. We propose an algorithm for semantic segmentation based on 3D point clouds derived from ego-motion. We motivate five simple cues designed to model specific patterns of motion and 3D world structure that vary with object category. We introduce features that project the 3D cues back to the 2D image plane while modeling spatial layout and context. A randomized decision forest combines many such features to achieve a coherent 2D segmentation and recognize the object categories present. Our main contribution is to show how semantic segmentation is possible based solely on motion-derived 3D world structure. Our method works well on sparse, noisy point clouds, and unlike existing approaches, does not need appearance-based descriptors. Experiments were performed on a challenging new video database containing sequences filmed from a moving car in daylight and at dusk. The results confirm that indeed, accurate segmentation and recognition are possible using only motion and 3D world structure. Further, we show that the motion-derived information complements an existing state-of-the-art appearance-based method, improving both qualitative and quantitative performance. input video frame reconstructed 3D point cloud automatic segmentation Fig. 1. The proposed algorithm uses 3D point clouds estimated from videos such as the pictured driving sequence (with ground truth inset). Having trained on point clouds from other driving sequences, our new motion and structure features, based purely on the point cloud, perform 11-class semantic segmentation of each test frame. The colors in the ground truth and inferred segmentation indicate category labels. 2 1

Pedestrian detection is a rapidly evolving area in computer vision with key applications in intelligent vehicles, surveillance and advanced robotics. The objective of this paper is to provide an overview of the current state of the art from both methodological and experimental perspective. The first part of the paper consists of a survey. We cover the main components of a pedestrian detection system and the underlying models. The second (and larger) part of the paper contains a corresponding experimental study. We consider a diverse set of state-of-the-art systems: wavelet-based AdaBoost cascade [74], HOG/linSVM [11], NN/LRF [75] and combined shape-texture detection [23]. Experiments are performed on an extensive dataset captured on-board a vehicle driving through urban environment. The dataset includes many thousands of training samples as well as a 27 minute test sequence involving more than 20000 images with annotated pedestrian locations. We consider a generic evaluation setting and one specific to pedestrian detection on-board a vehicle. Results indicate a clear advantage of HOG/linSVM at higher image resolutions and lower processing speeds, and a superiority of the wavelet-based AdaBoost cascade approach at lower image resolutions and (near) real-time processing speeds. The dataset (8.5GB) is made public for benchmarking purposes.

Abstract. The use of sparse invariant features to recognise classes of actions or objects has become common in the literature. However, features are often ”engineered ” to be both sparse and invariant to transformation and it is assumed that they provide the greatest discriminative information. To tackle activity recognition, we propose learning compound features that are assembled from simple 2D corners in both space and time. Each corner is encoded in relation to its neighbours and from an over complete set (in excess of 1 million possible features), compound features are extracted using data mining. The final classifier, consisting of sets of compound features, can then be applied to recognise and localise an activity in real-time while providing superior performance to other state-of-the-art approaches (including those based upon sparse feature detectors). Furthermore, the approach requires only weak supervision in the form of class labels for each training sequence. No ground truth position or temporal alignment is required during training. 1

Within the field of action recognition, features and descriptors are often engineered to be sparse and invariant to transformation. While sparsity makes the problem tractable, it is not necessarily optimal in terms of class separability and classification. This paper proposes a novel approach that uses very dense corner features that are spatially and temporally grouped in a hierarchical process to produce an overcomplete compound feature set. Frequently reoccurring patterns of features are then found through data mining, designed for use with large data sets. The novel use of the hierarchical classifier allows real time operation while the approach is demonstrated to handle camera motion, scale, human appearance variations, occlusions and background clutter. The performance of classification, outperforms other state-of-the-art action recognition algorithms on the three datasets; KTH, multi-KTH, and realworld movie sequences containing broad actions. Multiple action localisation is performed, though no groundtruth localisation data is required, using only weak supervision of class labels for each training sequence. The realworld movie dataset contain complex realistic actions from movies, the approach outperforms the published accuracy on this dataset and also achieves real time performance. 1.

How predictable are human eye movements during search in real world scenes? We recorded 14 observers ’ eye movements as they performed a search task (person detection) in 912 outdoor scenes. Observers were highly consistent in the regions fixated during search, even when the target was absent from the scene. These eye movements were used to evaluate computational models of search guidance from three sources: saliency, target features, and scene context. Each of these models independently outperformed a cross-image control in predicting human fixations. Models that combined sources of guidance ultimately predicted 94 % of human agreement, with the scene context component providing the most explanatory power. None of the models, however, could reach the precision and fidelity of an attentional map defined by human fixations. This work puts forth a benchmark for computational models of search in real world scenes. Further improvements in Please address all correspondence to Aude Oliva, Department of Brain and Cognitive

This paper presents a novel mixture-of-experts framework for pedestrian classification with partial occlusion handling. The framework involves a set of component-based expert classifiers trained on features derived from intensity, depth and motion. To handle partial occlusion, we compute expert weights that are related to the degree of visibility of the associated component. This degree of visibility is determined by examining occlusion boundaries, i.e. discontinuities in depth and motion. Occlusion-dependent component weights allow to focus the combined decision of the mixtureof-experts classifier on the unoccluded body parts. In experiments on extensive real-world data sets, with both partially occluded and non-occluded pedestrians, we obtain significant performance boosts over state-of-the-art approaches by up to a factor of four in reduction of false positives at constant detection rates. The dataset is made public for benchmarking purposes. 1.