Abid Ali

Institut national de recherche en informatique et en automatique

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Susanne Thümmler

Université Côte d'Azur

François Brémond

Institut national de recherche en informatique et en automatique

Jean‐Marc Odobez

Idiap Research Institute

Naveed Hussain

University of Central Punjab

Faiza Iqbal

University of Engineering and Technology Lahore

S.Sivachandiran

Camilla Barbini

Cognition Behaviour Technology

Ibrar Hussain

University of Lahore

Rui Dai

Idiap Research Institute

Mohammad Kaleem

COMSATS University Islamabad

Cooperative Institutions

Institut national de recherche en informatique et en automatique

Université Côte d'Azur

Observatoire de la Côte d’Azur

Cognition Behaviour Technology

University of Lahore

COMSATS University Islamabad

Research Centre Inria Sophia Antipolis - Méditerranée

Institut de Biologie Valrose

Centre de Recherche en Informatique

Institut de Chimie de Nice

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Weakly-Supervised Autism Severity Assessment in Long Videos

Abid Ali Mahmoud Ali Camilla Barbini Séverine Dubuisson Jean‐Marc Odobez

Autism Spectrum Disorder (ASD) is a diverse collection of neurobiological conditions marked by challenges in social communication and reciprocal interactions, as well as repetitive and stereotypical behaviors. Atypical behavior patterns in a long, untrimmed video can serve as biomarkers for children with ASD. In this paper, we propose a video-based weakly-supervised method that takes spatio-temporal features of long videos to learn typical and atypical behaviors for autism detection. On top of that, we propose a shallow TCN-MLP network, which is designed to further categorize the severity score. We evaluate our method on actual evaluation videos of children with autism collected and annotated (for severity score) by clinical professionals. Experimental results demonstrate the effectiveness of behavioral biomarkers that could help clinicians in autism spectrum analysis.

Reciprocal

10.1109/cbmi62980.2024.10859216

Cite

Citations (0)

Video-based Behavior Understanding of Children for Objective Diagnosis of Autism

Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2022)

Abid Ali Farhood Negin Susanne Thümmler François Brémond

One of the major diagnostic criteria for Autism Spectrum Disorder (ASD) is the recognition of stereotyped behaviors. However, it primarily relies on parental interviews and clinical observations, which result in a prolonged diagnosis cycle preventing ASD children from timely treatment. To help clinicians speed up the diagnosis process, we propose a computer-vision-based solution. First, we collected and annotated a novel dataset for action recognition tasks in videos of children with ASD in an uncontrolled environment. Second, we propose a multi-modality fusion network based on 3D CNNs. In the first stage of our method, we pre-process the RGB videos to get the ROI (child) using Yolov5 and DeepSORT algorithms. For optical flow extraction, we use the RAFT algorithm. In the second stage, we perform extensive experiments on different deep learning frameworks to propose a baseline. In the last stage, a multi-modality-based late fusion network is proposed to classify and evaluate performance of ASD children. The results revealed that the multi-modality fusion network achieves the best accuracy as compared to other methods. The baseline results also demonstrate the potential of an action-recognition-based system to assist clinicians in a reliable, accurate, and timely diagnosis of ASD disorder.

Modality (human–computer interaction)

Baseline (sea)

10.5220/0010839200003124

Cite

Citations (16)

Weakly-supervised Autism Severity Assessment in Long Videos

Abid Ali

Reciprocal

10.31234/osf.io/bp74z

Cite

Citations (0)

Supplemental Information 4: Code for analyzing the DSR method using the Yelp dataset.

PeerJ (2021)

Naveed Hussain Hamid Turab Mirza Abid Ali Faiza Iqbal Ibrar Hussain

Code (set theory)

10.7717/peerj-cs.472/supp-4

Cite

Citations (0)

Loose Social-Interaction Recognition in Real-world Therapy Scenarios

arXiv (Cornell University) (2025)

Abid Ali Rui Dai Ashish Marisetty Guillaume Astruc Monique Thonnat

The computer vision community has explored dyadic interactions for atomic actions such as pushing, carrying-object, etc. However, with the advancement in deep learning models, there is a need to explore more complex dyadic situations such as loose interactions. These are interactions where two people perform certain atomic activities to complete a global action irrespective of temporal synchronisation and physical engagement, like cooking-together for example. Analysing these types of dyadic-interactions has several useful applications in the medical domain for social-skills development and mental health diagnosis. To achieve this, we propose a novel dual-path architecture to capture the loose interaction between two individuals. Our model learns global abstract features from each stream via a CNNs backbone and fuses them using a new Global-Layer-Attention module based on a cross-attention strategy. We evaluate our model on real-world autism diagnoses such as our Loose-Interaction dataset, and the publicly available Autism dataset for loose interactions. Our network achieves baseline results on the Loose-Interaction and SOTA results on the Autism datasets. Moreover, we study different social interactions by experimenting on a publicly available dataset i.e. NTU-RGB+D (interactive classes from both NTU-60 and NTU-120). We have found that different interactions require different network designs. We also compare a slightly different version of our method by incorporating time information to address tight interactions achieving SOTA results.

RGB color model

Baseline (sea)

Source

Cite

Citations (0)

A SCORE BASED RANKING ALGORITHM FOR PRODUCT TRACKING SYSTEM

International Journal For Science Technology And Engineering (2015)

V.Boopathirajan Abid Ali S.Sivachandiran

Tracking (education)

Source

Cite

Citations (0)