MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving

by   Mennatullah Siam, et al.

We propose a novel multi-task learning system that combines appearance and motion cues for a better semantic reasoning of the environment. A unified architecture for joint vehicle detection and motion segmentation is introduced. In this architecture, a two-stream encoder is shared among both tasks. In order to evaluate our method in autonomous driving setting, KITTI annotated sequences with detection and odometry ground truth are used to automatically generate static/dynamic annotations on the vehicles. This dataset is called KITTI Moving Object Detection dataset (KITTI MOD). The dataset will be made publicly available to act as a benchmark for the motion detection task. Our experiments show that the proposed method outperforms state of the art methods that utilize motion cue only with 21.5 the state of the art unsupervised methods on DAVIS benchmark for generic object segmentation. One of our interesting conclusions is that joint training of motion segmentation and vehicle detection benefits motion segmentation. Motion segmentation has relatively fewer data, unlike the detection task. However, the shared fusion encoder benefits from joint training to learn a generalized representation. The proposed method runs in 120 ms per frame, which beats the state of the art motion detection/segmentation in computational efficiency.


page 1

page 2

page 3

page 5

page 6


MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

While most approaches to semantic reasoning have focused on improving pe...

Spatio-Temporal Multi-Task Learning Transformer for Joint Moving Object Detection and Segmentation

Moving objects have special importance for Autonomous Driving tasks. Det...

MODETR: Moving Object Detection with Transformers

Moving Object Detection (MOD) is a crucial task for the Autonomous Drivi...

RST-MODNet: Real-time Spatio-temporal Moving Object Detection for Autonomous Driving

Moving Object Detection (MOD) is a critical task for autonomous vehicles...

BEV-MODNet: Monocular Camera based Bird's Eye View Moving Object Detection for Autonomous Driving

Detection of moving objects is a very important task in autonomous drivi...

InstanceMotSeg: Real-time Instance Motion Segmentation for Autonomous Driving

Moving object segmentation is a crucial task for autonomous vehicles as ...

Discovering Objects that Can Move

This paper studies the problem of object discovery – separating objects ...

Please sign up or login with your details

Forgot password? Click here to reset