Fast Globally Optimal 2D Human Detection with Loopy Graph Models

This paper presents an algorithm for recovering the globally optimal 2D human figure detection using a loopy graph model. This is computationally challenging because the time complexity scales exponentially in the size of the largest clique in the graph. The proposed algorithm uses Branch and Bound (BB) to search for the globally optimal solution. The algorithm converges rapidly in practice and this is due to a novel method for quickly computing tree based lower bounds. The key idea is to recycle the dynamic programming (DP) tables associated with the tree model to look up the tree based lower bound rather than recomputing the lower bound from scratch. This technique is further sped up using Range Minimum Query data structures to provide $O(1)$ cost for computing the lower bound for most iterations of the BB algorithm. The algorithm is evaluated on the Iterative Parsing dataset and it is shown to run fast empirically

Efficient techniques for recovering 2D human body poses from images (PhD thesis)

Human parsing recovers the 2D spatial layout of a human figure in an image. First, patches in the image that resemble body parts, i.e., head, torso and limbs, are identified, then a coherent human figure is assembled from these candidate positions. The human model is represented as a graph where each vertex represents a body part and each edge represents a relationship between parts. If the graph is a tree, then the optimal solution can be recovered efficiently using the Min-Sum (MS) algorithm. Tree models often return incorrect solutions with the left and right legs stacked on top of one another. To overcome this problem, we add constraints to the tree model, yielding a graph that contains loops. Finding the optimal solution for a loopy graph is computationally intensive. We propose a Branch and Bound search algorithm to recover the optimal solution. Our algorithm converges quickly in practice due to a novel tree structured lower bound and a fast way for evaluating these lower bounds. Naively, evaluating each lower bound requires $O(nh)$ time for a graph with $n$ vertices and $h$ candidate body part locations. We develop an $O(1)$ time method for evaluating the lower bound (in most iterations of the algorithm) by reusing messages from the MS algorithm and using a Range Minimum Query data structure. We also propose a human parsing model that encodes the viewpoint and walking phase of the human figure using the Common Factor Model (CFM). The main computational bottleneck of the CFM human parsing algorithm involves message creation for each iteration of the MS algorithm. The original CFM inference requires $O(kn)$ messages to be created for $k$ iterations of the MS algorithm in a graph with $n$ vertices. Our new algorithm reduces this to $O(n)$ messages created. This speedup is based on the insight that the messages are shifted from one iteration to the next and, therefore, messages can be created once and then shifted in subsequent iterations (shifting is an efficient operation which requires $O(1)$ time). In our experiments, the two proposed algorithms yield an order of magnitude computational speedup over competing algorithms

Combining Local Appearance and Holistic View: Dual-Source Deep Neural Networks for Human Pose Estimation

We propose a new learning-based method for estimating 2D human pose from a single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN). Recently, many methods have been developed to estimate human pose by using pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective. In this paper, we propose to integrate both the local (body) part appearance and the holistic view of each local part for more accurate human pose estimation. Specifically, the proposed DS-CNN takes a set of image patches (category-independent object proposals for training and multi-scale sliding windows for testing) as the input and then learns the appearance of each local part by considering their holistic views in the full body. Using DS-CNN, we achieve both joint detection, which determines whether an image patch contains a body joint, and joint localization, which finds the exact location of the joint in the image patch. Finally, we develop an algorithm to combine these joint detection/localization results from all the image patches for estimating the human pose. The experimental results show the effectiveness of the proposed method by comparing to the state-of-the-art human-pose estimation methods based on pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective.Comment: CVPR 201

Analyzing Structured Scenarios by Tracking People and Their Limbs

The analysis of human activities is a fundamental problem in computer vision. Though complex, interactions between people and their environment often exhibit a spatio-temporal structure that can be exploited during analysis. This structure can be leveraged to mitigate the effects of missing or noisy visual observations caused, for example, by sensor noise, inaccurate models, or occlusion. Trajectories of people and their hands and feet, often sufficient for recognition of human activities, lead to a natural qualitative spatio-temporal description of these interactions. This work introduces the following contributions to the task of human activity understanding: 1) a framework that efficiently detects and tracks multiple interacting people and their limbs, 2) an event recognition approach that integrates both logical and probabilistic reasoning in analyzing the spatio-temporal structure of multi-agent scenarios, and 3) an effective computational model of the visibility constraints imposed on humans as they navigate through their environment. The tracking framework mixes probabilistic models with deterministic constraints and uses AND/OR search and lazy evaluation to efficiently obtain the globally optimal solution in each frame. Our high-level reasoning framework efficiently and robustly interprets noisy visual observations to deduce the events comprising structured scenarios. This is accomplished by combining First-Order Logic, Allen's Interval Logic, and Markov Logic Networks with an event hypothesis generation process that reduces the size of the ground Markov network. When applied to outdoor one-on-one basketball videos, our framework tracks the players and, guided by the game rules, analyzes their interactions with each other and the ball, annotating the videos with the relevant basketball events that occurred. Finally, motivated by studies of spatial behavior, we use a set of features from visibility analysis to represent spatial context in the interpretation of human spatial activities. We demonstrate the effectiveness of our representation on trajectories generated by humans in a virtual environment

Semantic Localization and Mapping in Robot Vision

Integration of human semantics plays an increasing role in robotics tasks such as mapping, localization and detection. Increased use of semantics serves multiple purposes, including giving computers the ability to process and present data containing human meaningful concepts, allowing computers to employ human reasoning to accomplish tasks. This dissertation presents three solutions which incorporate semantics onto visual data in order to address these problems. First, on the problem of constructing topological maps from sequence of images. The proposed solution includes a novel image similarity score which uses dynamic programming to match images using both appearance and relative positions of local features simultaneously. An MRF is constructed to model the probability of loop-closures and a locally optimal labeling is found using Loopy-BP. The recovered loop closures are then used to generate a topological map. Results are presented on four urban sequences and one indoor sequence. The second system uses video and annotated maps to solve localization. Data association is achieved through detection of object classes, annotated in prior maps, rather than through detection of visual features. To avoid the caveats of object recognition, a new representation of query images is introduced consisting of a vector of detection scores for each object class. Using soft object detections, hypotheses about pose are refined through particle filtering. Experiments include both small office spaces, and a large open urban rail station with semantically ambiguous places. This approach showcases a representation that is both robust and can exploit the plethora of existing prior maps for GPS-denied environments while avoiding the data association problems encountered when matching point clouds or visual features. Finally, a purely vision-based approach for constructing semantic maps given camera pose and simple object exemplar images. Object response heatmaps are combined with known pose to back-project detection information onto the world. These update the world model, integrating information over time as the camera moves. The approach avoids making hard decisions on object recognition, and aggregates evidence about objects in the world coordinate system. These solutions simultaneously showcase the contribution of semantics in robotics and provide state of the art solutions to these fundamental problems