Basim Zafar

Bio: Basim Zafar is an academic researcher from Umm al-Qura University. The author has contributed to research in topics: Crowds & Crowd simulation. The author has an hindex of 5, co-authored 14 publications receiving 189 citations.

Virtual Tawaf: A case study in simulating the behavior of dense, heterogeneous crowds

Abstract: We present a system to simulate the movement of individual agents in large-scale crowds performing the Tawaf. The Tawaf serves as a unique test case. The crowd consists of a heterogeneous set of pilgrims, varying with respect to physical capacity as well as activity. Furthermore, the density of the crowd reaches very high levels. Our approach uses a finite state machine to specify the behavior of the agents at each time step in conjunction with a geometric, agent-based algorithm to specify how an agent interacts with its local neighbors to generate collision-free trajectories. The overall system can model agents with varying age, gender and behaviors, supporting the heterogeniety observed in the performance of the Tawaf, even at high densities.

Velocity-based modeling of physical interactions in dense crowds

Abstract: We present an interactive algorithm to model physics-based interactions in dense crowds. Our approach is capable of modeling both physical forces and interactions between agents and obstacles, while also allowing the agents to anticipate and avoid upcoming collisions during local navigation. We combine velocity-based collision-avoidance algorithms with external physical forces. The overall formulation produces various effects of forces acting on agents and crowds, including balance recovery motion and force propagation through the crowd. We further extend our method to model more complex behaviors involving social and cultural rules. We use finite-state machines to specify a series of behaviors and demonstrate our approach on many complex scenarios. Our algorithm can simulate a few thousand agents at interactive rates and can generate many emergent behaviors.

Right of way

Abstract: Pedestrian models typically represent interactions between agents in a symmetric fashion. In general, these symmetric relationships are valid for a large number of crowd simulation scenarios. However, there are many cases in which symmetric responses between agents are inappropriate, leading to unrealistic behavior or undesirable simulation artifacts. We present a novel formulation, called right of way, which provides a well-disciplined mechanism for modeling asymmetric relationships between pedestrians. Right of way is a general principle, which can be applied to different types of pedestrian models. We illustrate this by applying right of way to three different pedestrian models (two based on social forces and one based on velocity obstacles) and show its impact in multiple scenarios. Particularly, we show how it enables simulation of the complex relationships exhibited by pilgrims performing the Islamic religious ritual, the Tawaf.

Virtual Tawaf: A Velocity-Space-Based Solution for Simulating Heterogeneous Behavior in Dense Crowds

Abstract: We present a system to simulate the movement of individual agents in large-scale crowds performing the Tawaf. The Tawaf serves as a unique test case; the large crowd consists of a heterogeneous set of pilgrims, varying in both physical capacity and activity. Furthermore, the density of the crowd reaches extremely high levels (up to 8 people/m2). This extreme density can place impractical constraints on simulation parameters. We use a velocity-space-based pedestrian model which exhibits consistent results even under extreme density: reciprocal velocity obstacles (RVO). Furthermore, we extend RVO to include priority and right of way—agents respond to potential collisions asymmetrically depending on context; one agent may yield, to varying degrees, to another. Our system uses a finite state machine to specify the behavior of the agents at each time step, to model the varied behaviors seen during the Tawaf. The finite-state machine, used in conjunction with RVO, generates collision-free trajectories for tens of thousands of agents in the performance of the Tawaf. The overall system can model agents with varying age, gender and behaviors, supporting the heterogeneity observed in the performance of the Tawaf, even at high densities.

Towards accelerated agent-based crowd simulation for Hajj and Umrah

Abstract: There are many scientific applications ranging from weather prediction to oil and gas exploration that requires high-performance computing It aids industries and researchers to enrich further their advancements With the advent of general purpose computing over GPUs, most of the applications above are shifting towards High-Performance Computing (HPC) Agent-based crowd simulation is one of the candidates that requires high-performance computing This type of application is used to predict crowd movement in highly congested areas One of the most crucial scenarios in which this application can be used is to mimic the movement of the multi-cultural crowd performing Hajj and Umrah in Masjid Al-Haram, Makkah Adequate performance for an agent-based crowd system is a common problem in computer science While the existing event planning software, specifically for Hajj and Umrah, are unable to provide the required performance The main reason is the increasing amount of autonomous pilgrims every year In this paper, we propose a high performance agent-based crowd simulation that represents pilgrim movement during these rituals The performance is achieved by parallelizing an open source steering library called OpenSteer using CUDA over GPU By using our technique, event organizers will be able to simulate large crowds and will also be able to predict whether the developed event plan is viable or not We have also discussed the architecture and implementation of this parallel Hajj simulation

A CNN-assisted enhanced audio signal processing for speech emotion recognition

Abstract: Speech is the most significant mode of communication among human beings and a potential method for human-computer interaction (HCI) by using a microphone sensor. Quantifiable emotion recognition using these sensors from speech signals is an emerging area of research in HCI, which applies to multiple applications such as human-reboot interaction, virtual reality, behavior assessment, healthcare, and emergency call centers to determine the speaker's emotional state from an individual's speech. In this paper, we present major contributions for; (i) increasing the accuracy of speech emotion recognition (SER) compared to state of the art and (ii) reducing the computational complexity of the presented SER model. We propose an artificial intelligence-assisted deep stride convolutional neural network (DSCNN) architecture using the plain nets strategy to learn salient and discriminative features from spectrogram of speech signals that are enhanced in prior steps to perform better. Local hidden patterns are learned in convolutional layers with special strides to down-sample the feature maps rather than pooling layer and global discriminative features are learned in fully connected layers. A SoftMax classifier is used for the classification of emotions in speech. The proposed technique is evaluated on Interactive Emotional Dyadic Motion Capture (IEMOCAP) and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) datasets to improve accuracy by 7.85% and 4.5%, respectively, with the model size reduced by 34.5 MB. It proves the effectiveness and significance of the proposed SER technique and reveals its applicability in real-world applications.

Towards a Socially Acceptable Collision Avoidance for a Mobile Robot Navigating Among Pedestrians Using a Pedestrian Model

Abstract: Safe navigation is a fundamental capability for robots that move among pedestrians. The traditional approach in robotics to attain such a capability has treated pedestrians as moving obstacles and provides algorithms that assure collision-free motion in the presence of such moving obstacles. In contrast, recent studies have focused on providing the robot not only collision-free motion but also a socially acceptable behavior by planning the robot’s path to maintain a “social distance” from pedestrians and respect their personal space. Such a social behavior is perceived as natural by the pedestrians and thus provides them a comfortable feeling, even if it may be considered a decorative element from a strictly safety oriented perspective. In this work we develop a system that realizes human-like collision avoidance in a mobile robot. In order to achieve this goal, we use a pedestrian model from human science literature, a version of the popular Social Force Model that was specifically designed to reproduce conditions similar to those found in shopping malls and other pedestrians facilities. Our findings show that the proposed system, which we tested in 2-h field trials in a real world environment, not only is perceived as comfortable by pedestrians but also yields safer navigation than traditional collision-free methods, since it better fits the behavior of the other pedestrians in the crowd.

Deep features-based speech emotion recognition for smart affective services

Abstract: Emotion recognition from speech signals is an interesting research with several applications like smart healthcare, autonomous voice response systems, assessing situational seriousness by caller affective state analysis in emergency centers, and other smart affective services. In this paper, we present a study of speech emotion recognition based on the features extracted from spectrograms using a deep convolutional neural network (CNN) with rectangular kernels. Typically, CNNs have square shaped kernels and pooling operators at various layers, which are suited for 2D image data. However, in case of spectrograms, the information is encoded in a slightly different manner. Time is represented along the x-axis and y-axis shows frequency of the speech signal, whereas, the amplitude is indicated by the intensity value in the spectrogram at a particular position. To analyze speech through spectrograms, we propose rectangular kernels of varying shapes and sizes, along with max pooling in rectangular neighborhoods, to extract discriminative features. The proposed scheme effectively learns discriminative features from speech spectrograms and performs better than many state-of-the-art techniques when evaluated its performance on Emo-DB and Korean speech dataset.

BRVO: Predicting pedestrian trajectories using velocity-space reasoning

Abstract: We introduce a novel, online method to predict pedestrian trajectories using agent-based velocity-space reasoning for improved human-robot interaction and collision-free navigation. Our formulation uses velocity obstacles to model the trajectory of each moving pedestrian in a robot's environment and improves the motion model by adaptively learning relevant parameters based on sensor data. The resulting motion model for each agent is computed using statistical inferencing techniques, including a combination of ensemble Kalman filters and a maximum-likelihood estimation algorithm. This allows a robot to learn individual motion parameters for every agent in the scene at interactive rates. We highlight the performance of our motion prediction method in real-world crowded scenarios, compare its performance with prior techniques, and demonstrate the improved accuracy of the predicted trajectories. We also adapt our approach for collision-free robot navigation among pedestrians based on noisy data and highlight the results in our simulator.