32,581 research outputs found
Autonomous monitoring of cliff nesting seabirds using computer vision
In this paper we describe a proposed system for automatic visual monitoring of seabird populations. Image sequences of cliff face nesting sites are captured using time-lapse digital photography. We are developing image processing software which is designed to automatically interpret these images, determine the number of birds present, and monitor activity. We focus primarily on the the development of low-level image processing techniques to support this goal. We first describe our existing work in video processing, and show how it is suitable for this problem domain. Image samples from a particular nest site are presented, and used to describe the associated challenges. We conclude by showing how we intend to develop our work to construct a distributed system capable of simultaneously monitoring a number of sites in the same locality
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
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