16 citations · 38 across the 8 of their papers we have counts for
12 papers
IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenes
Shubham Dokania, A. H. Abdul Hafez, Anbumani Subramanian +2
Autonomous driving and assistance systems rely on annotated data from traffic and road scenarios to model and learn the various object relations in complex real-world scenarios. Pr…
Detecting, Tracking and Counting Motorcycle Rider Traffic Violations on Unconstrained Roads
Aman Goyal, Dev Agarwal, Anbumani Subramanian +3
In many Asian countries with unconstrained road traffic conditions, driving violations such as not wearing helmets and triple-riding are a significant source of fatalities involvin…
Automatic Quantification and Visualization of Street Trees
Arpit Bahety, Rohit Saluja, Ravi Kiran Sarvadevabhatla +2
Assessing the number of street trees is essential for evaluating urban greenery and can help municipalities employ solutions to identify tree-starved streets. It can also help iden…
Meta Guided Metric Learner for Overcoming Class Confusion in Few-Shot Road Object Detection
Anay Majee, Anbumani Subramanian, Kshitij Agrawal
Localization and recognition of less-occurring road objects have been a challenge in autonomous driving applications due to the scarcity of data samples. Few-Shot Object Detection…
Multi-Domain Incremental Learning for Semantic Segmentation
Prachi Garg, Rohit Saluja, Vineeth N Balasubramanian +3
Recent efforts in multi-domain learning for semantic segmentation attempt to learn multiple geographical datasets in a universal, joint model. A simple fine-tuning experiment perfo…
Few-Shot Batch Incremental Road Object Detection via Detector Fusion
Anuj Tambwekar, Kshitij Agrawal, Anay Majee +1
Incremental few-shot learning has emerged as a new and challenging area in deep learning, whose objective is to train deep learning models using very few samples of new class data,…