Modern deep learning systems successfully solve many perception tasks such as object pose estimation when the input image is of high quality. However, in challenging imaging conditions such as on low-resolution images or when the image is corrupted by imaging artifacts, current systems degrade considerably in accuracy. While a loss in performance is unavoidable, we would like our models to quantify their uncertainty in order to achieve robustness against images of varying quality. Probabilistic deep learning models combine the expressive power of deep learning with uncertainty quantification. In this paper, we propose a novel probabilistic deep learning model for the task of angular regression. Our model uses von Mises distributions to predict a distribution over object pose angle. Whereas a single von Mises distribution is making strong assumptions about the shape of the distribution, we extend the basic model to predict a mixture of von Mises distributions. We show how to learn a mixture model using a finite and infinite number of mixture components. Our model allows for likelihood-based training and efficient inference at test time. We demonstrate on a number of challenging pose estimation datasets that our model produces calibrated probability predictions and competitive or superior point estimates compared to the current state-of-the-art.
has been a subject of extensive research in computer vision for a long time @cite_3 @cite_23 and the existing systems vary greatly in terms of feature representation and proposed classifiers. The input to pose estimation systems typically consists of 2D head images @cite_32 @cite_22 @cite_40 , and often one has to cope with low resolution images @cite_53 @cite_11 @cite_10 @cite_3 . Additional modalities such as depth @cite_45 and motion @cite_10 @cite_46 information has been exploited and provides useful cues. However, these are not always available. Also, information about the full body image could be used for joint head and body pose prediction @cite_42 @cite_48 @cite_31 . Notably the work of @cite_48 also promotes a probabilistic view and fuse body and head orientation within a tracking framework. Finally, the output of facial landmarks can be used as an intermediate step @cite_39 @cite_51 .
@cite_31 @cite_22 @cite_48 @cite_53 @cite_42 @cite_32 @cite_3 @cite_39 @cite_40 @cite_45 @cite_23 @cite_51 @cite_46 @cite_10 @cite_11
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Automatic head pose estimation from real-world video sequences is of great interest to the computer vision community since pose provides prior knowledge for tasks, such as face detection and classification. However, developing pose estimation algorithms requires large, labeled real-world video databases on which computer vision systems can be trained and tested. Manual labeling of each frame is tedious, time consuming, and often difficult due to the high uncertainty in head pose angle estimate, particularly in unconstrained environments that include arbitrary facial expression, occlusion, illumination etc. To overcome these difficulties, a semi-automatic framework is proposed for labeling temporal head pose in real-world video sequences. The proposed multi-stage labeling framework first detects a subset of frames with distinct head poses over a video sequence, which is then manually labeled by the expert to obtain the ground truth for those frames. The proposed framework provides a continuous head pose label and corresponding confidence value over the pose angles. Next, the interpolation scheme over a video sequence estimates i) labels for the frames without manual labels and ii) corresponding confidence values for interpolated labels. This confidence value permits an automatic head pose estimation framework to determine the subset of frames to be used for further processing, depending on the labeling accuracy required. The experiments performed on an in-house, labeled, large, real-world face video database (which will be made publicly available) show that the proposed framework achieves 96.98 labeling accuracy when manual labeling is only performed on 30 of the video frames. In this paper we consider people detection and articulated pose estimation, two closely related and challenging problems in computer vision. Conceptually, both of these problems can be addressed within the pictorial structures framework (Felzenszwalb and Huttenlocher in Int. J. Comput. Vis. 61(1):55---79, 2005; Fischler and Elschlager in IEEE Trans. Comput. C-22(1):67---92, 1973), even though previous approaches have not shown such generality. A principal difficulty for such a general approach is to model the appearance of body parts. The model has to be discriminative enough to enable reliable detection in cluttered scenes and general enough to capture highly variable appearance. Therefore, as the first important component of our approach, we propose a discriminative appearance model based on densely sampled local descriptors and AdaBoost classifiers. Secondly, we interpret the normalized margin of each classifier as likelihood in a generative model and compute marginal posteriors for each part using belief propagation. Thirdly, non-Gaussian relationships between parts are represented as Gaussians in the coordinate system of the joint between the parts. Additionally, in order to cope with shortcomings of tree-based pictorial structures models, we augment our model with additional repulsive factors in order to discourage overcounting of image evidence. We demonstrate that the combination of these components within the pictorial structures framework results in a generic model that yields state-of-the-art performance for several datasets on a variety of tasks: people detection, upper body pose estimation, and full body pose estimation. We present a hierarchical graphical model to probabilistically estimate head pose angles from real-world videos, that leverages the temporal pose information over video frames. The proposed model employs a number of complementary facial features, and performs feature level, probabilistic classifier level and temporal level fusion. Extensive experiments are performed to analyze the pose estimation performance for different combination of features, different levels of the proposed hierarchical model and for different face databases. Experiments show that the proposed head pose model improves on the current state-of-the-art for the unconstrained McGillFaces [10] and the constrained CMU Multi-PIE [14] databases, increasing the pose classification accuracy compared to the current top performing method by 19.38 and 19.89 , respectively. We present a probabilistic framework for the joint estimation of pedestrian head and body orientation from a mobile stereo vision platform. For both head and body parts, we convert the responses of a set of orientation-specific detectors into a (continuous) probability density function. The parts are localized by means of a pictorial structure approach, which balances part-based detector responses with spatial constraints. Head and body orientations are estimated jointly to account for anatomical constraints. The joint single-frame orientation estimates are integrated over time by particle filtering. The experiments involved data from a vehicle-mounted stereo vision camera in a realistic traffic setting; 65 pedestrian tracks were supplied by a state-of-the-art pedestrian tracker. We show that the proposed joint probabilistic orientation estimation framework reduces the mean absolute head and body orientation error up to 15° compared with simpler methods. This results in a mean absolute head body orientation error of about 21° 19°, which remains fairly constant up to a distance of 25 m. Our system currently runs in near real time (8–9 Hz).