Survey on Vision-based Path Prediction

Path prediction is a fundamental task for estimating how pedestrians or vehicles are going to move in a scene. Because path prediction as a task of computer vision uses video as input, various information used for prediction, such as the environment surrounding the target and the internal state of the target, need to be estimated from the video in addition to predicting paths. Many prediction approaches that include understanding the environment and the internal state have been proposed. In this survey, we systematically summarize methods of path prediction that take video as input and and extract features from the video. Moreover, we introduce datasets used to evaluate path prediction methods quantitatively.Comment: DAPI 201

Online Action Detection

In online action detection, the goal is to detect the start of an action in a video stream as soon as it happens. For instance, if a child is chasing a ball, an autonomous car should recognize what is going on and respond immediately. This is a very challenging problem for four reasons. First, only partial actions are observed. Second, there is a large variability in negative data. Third, the start of the action is unknown, so it is unclear over what time window the information should be integrated. Finally, in real world data, large within-class variability exists. This problem has been addressed before, but only to some extent. Our contributions to online action detection are threefold. First, we introduce a realistic dataset composed of 27 episodes from 6 popular TV series. The dataset spans over 16 hours of footage annotated with 30 action classes, totaling 6,231 action instances. Second, we analyze and compare various baseline methods, showing this is a challenging problem for which none of the methods provides a good solution. Third, we analyze the change in performance when there is a variation in viewpoint, occlusion, truncation, etc. We introduce an evaluation protocol for fair comparison. The dataset, the baselines and the models will all be made publicly available to encourage (much needed) further research on online action detection on realistic data.Comment: Project page: http://homes.esat.kuleuven.be/~rdegeest/OnlineActionDetection.htm

Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images

In this paper, we study the challenging problem of predicting the dynamics of objects in static images. Given a query object in an image, our goal is to provide a physical understanding of the object in terms of the forces acting upon it and its long term motion as response to those forces. Direct and explicit estimation of the forces and the motion of objects from a single image is extremely challenging. We define intermediate physical abstractions called Newtonian scenarios and introduce Newtonian Neural Network ($N^3$) that learns to map a single image to a state in a Newtonian scenario. Our experimental evaluations show that our method can reliably predict dynamics of a query object from a single image. In addition, our approach can provide physical reasoning that supports the predicted dynamics in terms of velocity and force vectors. To spur research in this direction we compiled Visual Newtonian Dynamics (VIND) dataset that includes 6806 videos aligned with Newtonian scenarios represented using game engines, and 4516 still images with their ground truth dynamics

Early Recognition of Human Activities from First-Person Videos Using Onset Representations

In this paper, we propose a methodology for early recognition of human activities from videos taken with a first-person viewpoint. Early recognition, which is also known as activity prediction, is an ability to infer an ongoing activity at its early stage. We present an algorithm to perform recognition of activities targeted at the camera from streaming videos, making the system to predict intended activities of the interacting person and avoid harmful events before they actually happen. We introduce the novel concept of 'onset' that efficiently summarizes pre-activity observations, and design an approach to consider event history in addition to ongoing video observation for early first-person recognition of activities. We propose to represent onset using cascade histograms of time series gradients, and we describe a novel algorithmic setup to take advantage of onset for early recognition of activities. The experimental results clearly illustrate that the proposed concept of onset enables better/earlier recognition of human activities from first-person videos

Three Concepts of Law: The Ambiguous Legacy of H.L.A. Hart

The law presents itself as a body of meaning, open to discovery, interpretation, application, criticism, development and change. But what sort of meaning does the law possess? Legal theory provides three sorts of answers. The first portrays the law as a mode of communication through which law-makers convey certain standards or norms to the larger community. The law's meaning is that imparted by its authors. On this view, law is a vehicle, conveying a message from a speaker to an intended audience. The second theory portrays the law as a mode of interpretation, whereby judges, officials, and ordinary citizens make decisions about how the law applies in various practical contexts. The law's meaning is that furnished by its interpreters. According to this theory, law is a receptacle into which decision-makers pour meaning. The third viewpoint argues that these theories, while not altogether wrong, are incomplete because they downplay or ignore the autonomous meaning that the law itself possesses. This theory suggests that the law is basically a mode of participation, whereby legislators, judges, officials, and ordinary people attune themselves to an autonomous field of legal meaning. The law's meaning is grounded in a body of social practice which is independent of both the law's authors and its interpreters and which is infused with basic values and principles that transcend the practice. On this view, law is the emblem of meaning that lies beyond it. Elements of all three theories are present in H.L.A. Hart's influential work, The Concept of Law, which attempts to fuse them into a single, all-encompassing theory. Nevertheless, as we will argue here, the attempt is not successful. Any true reconciliation of the communication and interpretation theories can only take place within the framework of a fully developed participation theory. In the early stages of his work, Hart lays the foundation for such a theory. However, his failure to elaborate it in a thoroughgoing way renders the work incomplete and ultimately unbalanced. As we will see, there is something to be learned from this failure