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MotionRec

This repository contains a Keras implementation of the paper 'MotionRec: A Unified Deep Framework for Moving Object Recognition' accepted in WACV 2020. To the best of our knowledge, this is a first attempt for simultaneous localization and classification of moving objects in a video, i.e. moving object recognition (MOR) in a single-stage deep learning framework.

Source Code Available Here

https://github.com/lav-kush/MotionRec

Description

Due to lack of available benchmark datasets with labelled bounding boxes for MOR, we created a new set of ground truths by annotating 42,614 objects (14,814 cars and 27,800 person) in 24,923 video frames from CDnet 2014 dataset. We selected 16 video sequences having 21,717 frames and 38,827 objects (13,442 cars and 25,385 person) for training. For testing, 3 video sequences with 3,206 frame and 3,787 objects (1,372 cars and 2,415 person) were chosen. We created axis-aligned bounding box annotations for moving object instances in all the frames.

Paper

MotionRec: A Unified Deep Framework for Moving Object Recognition

BibTex

@InProceedings{Mandal_2020_WACV, author = {Mandal, Murari and Kumar, Lav Kush and Saran, Mahipal Singh and vipparthi, Santosh Kumar}, title = {MotionRec: A Unified Deep Framework for Moving Object Recognition}, booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)}, month = {March}, year = {2020} }

Dataset-Link

Dataset: https://drive.google.com/file/d/1drn2PJryDlld7KEiN6eBZQaJJCfrevNz/view?usp=sharing

Labels: https://drive.google.com/file/d/185rTXUhAjmRBgMO_NG3M86KEjixP-kdK/view?usp=sharing

Contact: Murari Mandal(murarimandal.cv@gmail.com)

Sample Results