The ability for a teacher to engage all students in active learning processes in classroom constitutes a crucial prerequisite for enhancing students achievement. Teachers' attentional processes provide important insights into teachers' ability to focus their attention on relevant information in the complexity of classroom interaction and distribute their attention across students in order to recognize the relevant needs for learning. In this context, mobile eye tracking is an innovative approach within teaching effectiveness research to capture teachers' attentional processes while teaching. However, analyzing mobile eye-tracking data by hand is time consuming and still limited. In this paper, we introduce a new approach to enhance the impact of mobile eye tracking by connecting it with computer vision. In mobile eye tracking videos from an educational study using a standardized small group situation, we apply a state-ofthe-art face detector, create face tracklets, and introduce a novel method to cluster faces into the number of identity. Subsequently, teachers' attentional focus is calculated per student during a teaching unit by associating eye tracking fixations and face tracklets. To the best of our knowledge, this is the first work to combine computer vision and mobile eye tracking to model teachers' attention while instructing.
Face clustering is a widely studied topic and applied in still images and video tracklets, which are extracted from movies or TV series @cite_16 @cite_54 @cite_50 . Many previous studies applied face detection and created low-level tracklets by merging face detections and tracking. In clustering, methods which are based on hand-crafted features exploited additional cues to create must-link and must-not-link constraints to improve representation ability of learned feature space.
@cite_54 @cite_16 @cite_50
2150469677 1995032263 98220398 2150090564
We describe a novel method that simultaneously clusters and associates short sequences of detected faces (termed as face track lets) in videos. The rationale of our method is that face track let clustering and linking are related problems that can benefit from the solutions of each other. Our method is based on a hidden Markov random field model that represents the joint dependencies of cluster labels and track let linking associations. We provide an efficient algorithm based on constrained clustering and optimal matching for the simultaneous inference of cluster labels and track let associations. We demonstrate significant improvements on the state-of-the-art results in face tracking and clustering performances on several video datasets. Face clustering is an important but challenging task since facial images always have huge variation due to change in facial expressions, head poses and partial occlusions, etc. Moreover, face clustering is actually an unsupervised problem which makes it more difficult to reach an accurate result. Fortunately, there are some cues that can be used to improve clustering performance. In this paper, two types of cues are employed. The first one is pairwise constraints: must-link and cannot-link constraints, which can be extracted from the temporal and spatial knowledge of data. The other is that each face is associated with a series of attributes (i.e, gender) which can contribute discrimination among faces. To take advantage of the above cues, we propose a new algorithm, Multi-cue Augmented Face Clustering (McAFC), which effectively incorporates the cues via graph-guided sparse subspace clustering technique. Specially, facial images from the same individual are encouraged to be connected while faces from different persons are restrained to be connected. Experiments on three face datasets from real-world videos show the improvements of our algorithm over the state-of-the-art methods. In this paper, we study the problem of face clustering in videos. Specifically, given automatically extracted faces from videos and two kinds of prior knowledge (the face track that each face belongs to, and the pairs of faces that appear in the same frame), the task is to partition the faces into a given number of disjoint groups, such that each group is associated with one subject. To deal with this problem, we propose a new method called weighted block-sparse low rank representation (WBSLRR) which considers the available prior knowledge while learning a low rank data representation, and also develop a simple but effective approach to obtain the clustering result of faces. Moreover, after using several acceleration techniques, our proposed method is suitable for solving large-scale problems. The experimental results on two benchmark datasets demonstrate the effectiveness of our approach. We propose an approach for multi-pose face tracking by association of face detection responses in two stages using multiple cues. The low-level stage uses a two-threshold strategy to merge detection responses based on location, size and pose, resulting in short but reliable tracklets. The high-level stage uses different cues for computing a joint similarity measure between tracklets. The facial cue compares facial features of the most frontal face detections in pairs of tracklets. The classifier cue learns a discriminative appearance model for each tracklet, using detection pairs within reliable tracklets and between overlapping tracklets as training data. The constraint cue observes the compatibility of motion of two tracklets. The association of tracklets is globally optimized with the Hungarian algorithm. We validate our approach on two challenging episodes of two TV series and report a Multiple Object Tracking Accuracy (MOTA) of 82 and 68.2 , respectively.