Sejuti Rahman

Utsha Kumar Roy, Sejuti Rahman

Robots navigating human-populated environments must avoid collisions while respecting the social structure of crowds, particularly the implicit boundaries of social groups. Most navigation approaches model humans as independent individuals,causing socially disruptive behavior even when collision-free. This paper presents TAGA (Tangent Action for Group Avoidance), detected group boundaries via tangent-path maneuvers without modifying the underlying navigation policy. A hierarchical safety controller coordinates group-level avoidance with individual collision prevention. We propose the Group Crossing Rate (GCR), a continuous metric measuring the fraction of timesteps the robot spends inside any group convex hull, providing finer-grained social compliance assessment than terminal metrics alone. We introduce a realistic crowd simulation benchmark with five empirically grounded phases: individual speed heterogeneity, group speed coupling, F-formation static groups, leader-follower dynamics, and convex-hull boundaries, evaluated under both ORCA and Social Force pedestrian dynamics. Experiments across ORCA, Social Force, DS-RNN, and Intention-RL reveal a reactive-learning asymmetry: TAGA provides the largest gains for classical reactive baselines (up to +8pp success rate, GCR halved) with near-zero cost for learned policies. These findings offer actionable guidance for when modular group-awareness adds value versus when end-to-end group-aware training is preferable.

Shafin Rahman, Sejuti Rahman, Omar Shahid et al.

A plethora of research in the literature shows how human eye fixation pattern varies depending on different factors, including genetics, age, social functioning, cognitive functioning, and so on. Analysis of these variations in visual attention has already elicited two potential research avenues: 1) determining the physiological or psychological state of the subject and 2) predicting the tasks associated with the act of viewing from the recorded eye-fixation data. To this end, this paper proposes a visual saliency based novel feature extraction method for automatic and quantitative classification of eye-tracking data, which is applicable to both of the research directions. Instead of directly extracting features from the fixation data, this method employs several well-known computational models of visual attention to predict eye fixation locations as saliency maps. Comparing the saliency amplitudes, similarity and dissimilarity of saliency maps with the corresponding eye fixations maps gives an extra dimension of information which is effectively utilized to generate discriminative features to classify the eye-tracking data. Extensive experimentation using Saliency4ASD, Age Prediction, and Visual Perceptual Task dataset show that our saliency-based feature can achieve superior performance, outperforming the previous state-of-the-art methods by a considerable margin. Moreover, unlike the existing application-specific solutions, our method demonstrates performance improvement across three distinct problems from the real-life domain: Autism Spectrum Disorder screening, toddler age prediction, and human visual perceptual task classification, providing a general paradigm that utilizes the extra-information inherent in saliency maps for a more accurate classification.

Mohaiminul Al Nahian, A. S. M. Iftekhar, Mohammad Tariqul Islam et al.

In the Internet, ubiquitous presence of redundant, unedited, raw videos has made video summarization an important problem. Traditional methods of video summarization employ a heuristic set of hand-crafted features, which in many cases fail to capture subtle abstraction of a scene. This paper presents a deep learning method that maps the context of a video to the importance of a scene similar to that is perceived by humans. In particular, a convolutional neural network (CNN)-based architecture is proposed to mimic the frame-level shot importance for user-oriented video summarization. The weights and biases of the CNN are trained extensively through off-line processing, so that it can provide the importance of a frame of an unseen video almost instantaneously. Experiments on estimating the shot importance is carried out using the publicly available database TVSum50. It is shown that the performance of the proposed network is substantially better than that of commonly referred feature-based methods for estimating the shot importance in terms of mean absolute error, absolute error variance, and relative F-measure.