We introduce and evaluate a novel approach for detecting smooth pursuit eye movements that increases the number of distinguishable targets and is more robust against false positives. Being natural and calibration-free, Pursuits has been gaining popularity in the past years. At the same time, current implementations show poor performance when more than eight on-screen targets are being used, thus limiting its applicability. Our approach (1) leverages the slope of a regression line, and (2) introduces a minimum signal duration that improves both the new and the traditional detection method. After introducing the approach as well as the implementation, we compare it to the traditional correlation-based Pursuits detection method. We tested the approach up to 24 targets and show that, if accepting a similar error rate, nearly twice as many targets can be distinguished compared to state of the art. For fewer targets, accuracy increases significantly. We believe our approach will enable more robust pursuit-based user interfaces, thus making it valuable for both researchers and practitioners.
There are two predominant implementations of Pursuits detection for interaction, one of which uses the Euclidean distance between the gaze estimates and target positions @cite_8 @cite_9 @cite_13 @cite_15 , while the other one employ Pearson's product moment correlation @cite_14 @cite_18 @cite_6 @cite_23 @cite_16 @cite_2 @cite_4 @cite_20 @cite_17 .
@cite_18 @cite_14 @cite_4 @cite_8 @cite_9 @cite_6 @cite_23 @cite_2 @cite_15 @cite_16 @cite_13 @cite_20 @cite_17
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In this paper we show how reading text on large display can be used to enable gaze interaction in public space. Our research is motivated by the fact that much of the content on public displays includes text. Hence, researchers and practitioners could greatly benefit from users being able to spontaneously interact as well as to implicitly calibrate an eye tracker while simply reading this text. In particular, we adapt Pursuits, a technique that correlates users' eye movements with moving on-screen targets. While prior work used abstract objects or dots as targets, we explore the use of Pursuits with text (read-and-pursue). Thereby we address the challenge that eye movements performed while reading interfere with the pursuit movements. Results from two user studies (N=37) show that Pursuits with text is feasible and can achieve similar accuracy as non text-based pursuit approaches. While calibration is less accurate, it integrates smoothly with reading and allows areas of the display the user is looking at to be identified. The objective of this work is recognition and spatiotemporal localization of two-person interactions in video. Our approach is person-centric. As a first stage we track all upper bodies and heads in a video using a tracking-by-detection approach that combines detections with KLT tracking and clique partitioning, together with occlusion detection, to yield robust person tracks. We develop local descriptors of activity based on the head orientation (estimated using a set of pose-specific classifiers) and the local spatiotemporal region around them, together with global descriptors that encode the relative positions of people as a function of interaction type. Learning and inference on the model uses a structured output SVM which combines the local and global descriptors in a principled manner. Inference using the model yields information about which pairs of people are interacting, their interaction class, and their head orientation (which is also treated as a variable, enabling mistakes in the classifier to be corrected using global context). We show that inference can be carried out with polynomial complexity in the number of people, and describe an efficient algorithm for this. The method is evaluated on a new dataset comprising 300 video clips acquired from 23 different TV shows and on the benchmark UT--Interaction dataset. The ability to perceive possible interactions with the environment is a key capability of task-guided robotic agents. An important subset of possible interactions depends solely on the objects of interest and their position and orientation in the scene. We call these object-based interactions 0-order affordances and divide them among non-hidden and hidden whether the current configuration of an object in the scene renders its affordance directly usable or not. Conversely to other works, we propose that detecting affordances that are not directly perceivable increase the usefulness of robotic agents with manipulation capabilities, so that by appropriate manipulation they can modify the object configuration until the seeked affordance becomes available. In this paper we show how 0-order affordances depending on the geometry of the objects and their pose can be learned using a supervised learning strategy on 3D mesh representations of the objects allowing the use of the whole object geometry. Moreover, we show how the learned affordances can be detected in real scenes obtained with a low-cost depth sensor like the Microsoft Kinect through object recognition and 6D0F pose estimation and present results for both learning on meshes and detection on real scenes to demonstrate the practical application of the presented approach. In this paper, we first provide a new perspective to divide existing high performance object detection methods into direct and indirect regressions. Direct regression performs boundary regression by predicting the offsets from a given point, while indirect regression predicts the offsets from some bounding box proposals. In the context of multioriented scene text detection, we analyze the drawbacks of indirect regression, which covers the state-of-the-art detection structures Faster-RCNN and SSD as instances, and point out the potential superiority of direct regression. To verify this point of view, we propose a deep direct regression based method for multi-oriented scene text detection. Our detection framework is simple and effective with a fully convolutional network and one-step post processing. The fully convolutional network is optimized in an end-to-end way and has bi-task outputs where one is pixel-wise classification between text and non-text, and the other is direct regression to determine the vertex coordinates of quadrilateral text boundaries. The proposed method is particularly beneficial to localize incidental scene texts. On the ICDAR2015 Incidental Scene Text benchmark, our method achieves the F-measure of 81 , which is a new state-ofthe-art and significantly outperforms previous approaches. On other standard datasets with focused scene texts, our method also reaches the state-of-the-art performance. In this paper, we first provide a new perspective to divide existing high performance object detection methods into direct and indirect regressions. Direct regression performs boundary regression by predicting the offsets from a given point, while indirect regression predicts the offsets from some bounding box proposals. Then we analyze the drawbacks of the indirect regression, which the recent state-of-the-art detection structures like Faster-RCNN and SSD follows, for multi-oriented scene text detection, and point out the potential superiority of direct regression. To verify this point of view, we propose a deep direct regression based method for multi-oriented scene text detection. Our detection framework is simple and effective with a fully convolutional network and one-step post processing. The fully convolutional network is optimized in an end-to-end way and has bi-task outputs where one is pixel-wise classification between text and non-text, and the other is direct regression to determine the vertex coordinates of quadrilateral text boundaries. The proposed method is particularly beneficial for localizing incidental scene texts. On the ICDAR2015 Incidental Scene Text benchmark, our method achieves the F1-measure of 81 , which is a new state-of-the-art and significantly outperforms previous approaches. On other standard datasets with focused scene texts, our method also reaches the state-of-the-art performance. Most eye gaze estimation systems rely on explicit calibration, which is inconvenient to the user, limits the amount of possible training data and consequently the performance. Since there is likely a strong correlation between gaze and interaction cues, such as cursor and caret locations, a supervised learning algorithm can learn the complex mapping between gaze features and the gaze point by training on incremental data collected implicitly from normal computer interactions. We develop a set of robust geometric gaze features and a corresponding data validation mechanism that identifies good training data from noisy interaction-informed data collected in real-use scenarios. Based on a study of gaze movement patterns, we apply behavior-informed validation to extract gaze features that correspond with the interaction cue, and data-driven validation provides another level of crosschecking using previous good data. Experimental evaluation shows that the proposed method achieves an average error of 4.06o, and demonstrates the effectiveness of the proposed gaze estimation method and corresponding validation mechanism. We present a detection-based three-level hierarchical association approach to robustly track multiple objects in crowded environments from a single camera. At the low level, reliable tracklets (i.e. short tracks for further analysis) are generated by linking detection responses based on conservative affinity constraints. At the middle level, these tracklets are further associated to form longer tracklets based on more complex affinity measures. The association is formulated as a MAP problem and solved by the Hungarian algorithm. At the high level, entries, exits and scene occluders are estimated using the already computed tracklets, which are used to refine the final trajectories. This approach is applied to the pedestrian class and evaluated on two challenging datasets. The experimental results show a great improvement in performance compared to previous methods. Context has been playing an increasingly important role to improve the object detection performance. In this paper we propose an effective representation, Multi-Order Contextual co-Occurrence (MOCO), to implicitly model the high level context using solely detection responses from a baseline object detector. The so-called (1st-order) context feature is computed as a set of randomized binary comparisons on the response map of the baseline object detector. The statistics of the 1st-order binary context features are further calculated to construct a high order co-occurrence descriptor. Combining the MOCO feature with the original image feature, we can evolve the baseline object detector to a stronger context aware detector. With the updated detector, we can continue the evolution till the contextual improvements saturate. Using the successful deformable-part-model detector [13] as the baseline detector, we test the proposed MOCO evolution framework on the PASCAL VOC 2007 dataset [8] and Caltech pedestrian dataset [7]: The proposed MOCO detector outperforms all known state-of-the-art approaches, contextually boosting deformable part models (ver. 5) [13] by 3.3 in mean average precision on the PASCAL 2007 dataset. For the Caltech pedestrian dataset, our method further reduces the log-average miss rate from 48 to 46 and the miss rate at 1 FPPI from 25 to 23 , compared with the best prior art [6]. In this paper, the recognition of the visual focus of attention (VFOA) of meeting participants (as defined by their eye gaze direction) from their head pose is addressed. To this end, the head pose observations are modeled using an hidden Markov model (HMM) whose hidden states corresponds to the VFOA. The novelties are threefold. First, contrary to previous studies on the topic, in our set-up, the potential VFOA of a person is not restricted to other participants only, but includes environmental targets (a table and a projection screen), which increases the complexity of the task, with more VFOA targets spread in the pan and tilt (as well) gaze space. Second, the HMM parameters are set by exploiting results from the cognitive science on saccadic eye motion, which allows to predict what the head pose should be given an actual gaze target. Third, an unsupervised parameter adaptation step is proposed which accounts for the specific gazing behaviour of each participant. Using a publicly available corpus of 8 meetings featuring 4 persons, we analyze the above methods by evaluating, through objective performance measures, the recognition of the VFOA from head pose information obtained either using a magnetic sensor device or a vision based tracking system. In this paper, we address the challenging problem of simultaneous pedestrian detection and ground-plane estimation from video while walking through a busy pedestrian zone. Our proposed system integrates robust stereo depth cues, ground-plane estimation, and appearance-based object detection in a principled fashion using a graphical model. Object-object occlusions lead to complex interactions in this model that make an exact solution computationally intractable. We therefore propose a novel iterative approach that first infers scene geometry using belief propagation and then resolves interactions between objects using a global optimization procedure. This approach leads to a robust solution in few iterations, while allowing object detection to benefit from geometry estimation and vice versa. We quantitatively evaluate the performance of our proposed approach on several challenging test sequences showing strolls through busy shopping streets. Comparisons to various baseline systems show that it outperforms both a system using no scene geometry and one just relying on structure-from-motion without dense stereo. Eye contact plays a crucial role in our everyday social interactions. The ability of a device to reliably detect when a person is looking at it can lead to powerful human-object interfaces. Today, most gaze-based interactive systems rely on gaze tracking technology. Unfortunately, current gaze tracking techniques require active infrared illumination, calibration, or are sensitive to distance and pose. In this work, we propose a different solution-a passive, appearance-based approach for sensing eye contact in an image. By focusing on gaze *locking* rather than gaze tracking, we exploit the special appearance of direct eye gaze, achieving a Matthews correlation coefficient (MCC) of over 0.83 at long distances (up to 18 m) and large pose variations (up to ±30° of head yaw rotation) using a very basic classifier and without calibration. To train our detector, we also created a large publicly available gaze data set: 5,880 images of 56 people over varying gaze directions and head poses. We demonstrate how our method facilitates human-object interaction, user analytics, image filtering, and gaze-triggered photography. Pedestrian detection from images is an important and yet challenging task. The conventional methods usually identify human figures using image features inside the local regions. In this paper we present that, besides the local features, context cues in the neighborhood provide important constraints that are not yet well utilized. We propose a framework to incorporate the context constraints for detection. First, we combine the local window with neighborhood windows to construct a multi-scale image context descriptor, designed to represent the contextual cues in spatial, scaling, and color spaces. Second, we develop an iterative classification algorithm called contextual boost. At each iteration, the classifier responses from the previous iteration across the neighborhood and multiple image scales, called classification context, are incorporated as additional features to learn a new classifier. The number of iterations is determined in the training process when the error rate converges. Since the classification context incorporates contextual cues from the neighborhood, through iterations it implicitly propagates to greater areas and thus provides more global constraints. We evaluate our method on the Caltech benchmark dataset [11]. The results confirm the advantages of the proposed framework. Compared with state of the arts, our method reduces the miss rate from 29 by [30] to 25 at 1 false positive per image (FPPI). While most existing approaches for detection in videos focus on objects or human actions separately, we aim at jointly detecting objects performing actions, such as cat eating or dog jumping. We introduce an end-to-end multitask objective that jointly learns object-action relationships. We compare it with different training objectives, validate its effectiveness for detecting objects-actions in videos, and show that both tasks of object and action detection benefit from this joint learning. Moreover, the proposed architecture can be used for zero-shot learning of actions: our multitask objective leverages the commonalities of an action performed by different objects, e.g. dog and cat jumping, enabling to detect actions of an object without training with these object-actions pairs. In experiments on the A2D dataset [50], we obtain state-of-the-art results on segmentation of object-action pairs. We finally apply our multitask architecture to detect visual relationships between objects in images of the VRD dataset [24].