11,735 research outputs found
Understanding Traffic Density from Large-Scale Web Camera Data
Understanding traffic density from large-scale web camera (webcam) videos is
a challenging problem because such videos have low spatial and temporal
resolution, high occlusion and large perspective. To deeply understand traffic
density, we explore both deep learning based and optimization based methods. To
avoid individual vehicle detection and tracking, both methods map the image
into vehicle density map, one based on rank constrained regression and the
other one based on fully convolution networks (FCN). The regression based
method learns different weights for different blocks in the image to increase
freedom degrees of weights and embed perspective information. The FCN based
method jointly estimates vehicle density map and vehicle count with a residual
learning framework to perform end-to-end dense prediction, allowing arbitrary
image resolution, and adapting to different vehicle scales and perspectives. We
analyze and compare both methods, and get insights from optimization based
method to improve deep model. Since existing datasets do not cover all the
challenges in our work, we collected and labelled a large-scale traffic video
dataset, containing 60 million frames from 212 webcams. Both methods are
extensively evaluated and compared on different counting tasks and datasets.
FCN based method significantly reduces the mean absolute error from 10.99 to
5.31 on the public dataset TRANCOS compared with the state-of-the-art baseline.Comment: Accepted by CVPR 2017. Preprint version was uploaded on
http://welcome.isr.tecnico.ulisboa.pt/publications/understanding-traffic-density-from-large-scale-web-camera-data
Cascaded Scene Flow Prediction using Semantic Segmentation
Given two consecutive frames from a pair of stereo cameras, 3D scene flow
methods simultaneously estimate the 3D geometry and motion of the observed
scene. Many existing approaches use superpixels for regularization, but may
predict inconsistent shapes and motions inside rigidly moving objects. We
instead assume that scenes consist of foreground objects rigidly moving in
front of a static background, and use semantic cues to produce pixel-accurate
scene flow estimates. Our cascaded classification framework accurately models
3D scenes by iteratively refining semantic segmentation masks, stereo
correspondences, 3D rigid motion estimates, and optical flow fields. We
evaluate our method on the challenging KITTI autonomous driving benchmark, and
show that accounting for the motion of segmented vehicles leads to
state-of-the-art performance.Comment: International Conference on 3D Vision (3DV), 2017 (oral presentation
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