1,667 research outputs found
Multi-stream CNN based Video Semantic Segmentation for Automated Driving
Majority of semantic segmentation algorithms operate on a single frame even
in the case of videos. In this work, the goal is to exploit temporal
information within the algorithm model for leveraging motion cues and temporal
consistency. We propose two simple high-level architectures based on Recurrent
FCN (RFCN) and Multi-Stream FCN (MSFCN) networks. In case of RFCN, a recurrent
network namely LSTM is inserted between the encoder and decoder. MSFCN combines
the encoders of different frames into a fused encoder via 1x1 channel-wise
convolution. We use a ResNet50 network as the baseline encoder and construct
three networks namely MSFCN of order 2 & 3 and RFCN of order 2. MSFCN-3
produces the best results with an accuracy improvement of 9% and 15% for
Highway and New York-like city scenarios in the SYNTHIA-CVPR'16 dataset using
mean IoU metric. MSFCN-3 also produced 11% and 6% for SegTrack V2 and DAVIS
datasets over the baseline FCN network. We also designed an efficient version
of MSFCN-2 and RFCN-2 using weight sharing among the two encoders. The
efficient MSFCN-2 provided an improvement of 11% and 5% for KITTI and SYNTHIA
with negligible increase in computational complexity compared to the baseline
version.Comment: Accepted for Oral Presentation at VISAPP 201
Online Video Deblurring via Dynamic Temporal Blending Network
State-of-the-art video deblurring methods are capable of removing non-uniform
blur caused by unwanted camera shake and/or object motion in dynamic scenes.
However, most existing methods are based on batch processing and thus need
access to all recorded frames, rendering them computationally demanding and
time consuming and thus limiting their practical use. In contrast, we propose
an online (sequential) video deblurring method based on a spatio-temporal
recurrent network that allows for real-time performance. In particular, we
introduce a novel architecture which extends the receptive field while keeping
the overall size of the network small to enable fast execution. In doing so,
our network is able to remove even large blur caused by strong camera shake
and/or fast moving objects. Furthermore, we propose a novel network layer that
enforces temporal consistency between consecutive frames by dynamic temporal
blending which compares and adaptively (at test time) shares features obtained
at different time steps. We show the superiority of the proposed method in an
extensive experimental evaluation.Comment: 10 page
Learning 3D Human Pose from Structure and Motion
3D human pose estimation from a single image is a challenging problem,
especially for in-the-wild settings due to the lack of 3D annotated data. We
propose two anatomically inspired loss functions and use them with a
weakly-supervised learning framework to jointly learn from large-scale
in-the-wild 2D and indoor/synthetic 3D data. We also present a simple temporal
network that exploits temporal and structural cues present in predicted pose
sequences to temporally harmonize the pose estimations. We carefully analyze
the proposed contributions through loss surface visualizations and sensitivity
analysis to facilitate deeper understanding of their working mechanism. Our
complete pipeline improves the state-of-the-art by 11.8% and 12% on Human3.6M
and MPI-INF-3DHP, respectively, and runs at 30 FPS on a commodity graphics
card.Comment: ECCV 2018. Project page: https://www.cse.iitb.ac.in/~rdabral/3DPose
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