activity
20192021
most citedRoad User Detection in Videos

6 citations · 6 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV2021

PolyTrack: Tracking with Bounding Polygons

Gaspar Faure, Hughes Perreault, Guillaume-Alexandre Bilodeau +1

In this paper, we present a novel method called PolyTrack for fast multi-object tracking and segmentation using bounding polygons. Polytrack detects objects by producing heatmaps o…

cs.CV2021

FFAVOD: Feature Fusion Architecture for Video Object Detection

Hughes Perreault, Guillaume-Alexandre Bilodeau, Nicolas Saunier +1

A significant amount of redundancy exists between consecutive frames of a video. Object detectors typically produce detections for one image at a time, without any capabilities for…

cs.CV2021

CenterPoly: real-time instance segmentation using bounding polygons

Hughes Perreault, Guillaume-Alexandre Bilodeau, Nicolas Saunier +1

We present a novel method, called CenterPoly, for real-time instance segmentation using bounding polygons. We apply it to detect road users in dense urban environments, making it s…

cs.CV2020

RN-VID: A Feature Fusion Architecture for Video Object Detection

Hughes Perreault, Maguelonne Héritier, Pierre Gravel +2

Consecutive frames in a video are highly redundant. Therefore, to perform the task of video object detection, executing single frame detectors on every frame without reusing any in…

cs.CV2020

SpotNet: Self-Attention Multi-Task Network for Object Detection

Hughes Perreault, Guillaume-Alexandre Bilodeau, Nicolas Saunier +1

Humans are very good at directing their visual attention toward relevant areas when they search for different types of objects. For instance, when we search for cars, we will look…

cs.CV20196 cited

Road User Detection in Videos

Hughes Perreault, Guillaume-Alexandre Bilodeau, Nicolas Saunier +1

Successive frames of a video are highly redundant, and the most popular object detection methods do not take advantage of this fact. Using multiple consecutive frames can improve d…