Degraf-Flow: Extending Degraf Features for Accurate and Efficient Sparse-To-Dense Optical Flow Estimation
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Citations
Exploiting Semantic Information and Deep Matching for Optical Flow
References
Are we ready for autonomous driving? The KITTI vision benchmark suite
Determining optical flow
ORB: An efficient alternative to SIFT or SURF
Determining Optical Flow
A Database and Evaluation Methodology for Optical Flow
Related Papers (5)
Frequently Asked Questions (11)
Q2. What have the authors stated for future works in "Degraf-flow: extending degraf features for accurate and efficient sparse-to-dense optical flow estimation" ?
Future work will exploit the tracking of DeGraF features for applications in an autonomous vehicle setting.
Q3. What is the key to achieving accurate dense optical flow recovery?
sparse flow vectors with uniform spatial coverage is an ideal for accurate dense optical flow recovery [26] making uniform feature distribution across the scene a key conduit to success.
Q4. What data sets are used for the evaluation of dense optical flow?
Evaluation is carried out on the KITTI optic flow estimation benchmark data sets (denoted as KITTI 2012 [10] and KITTI 2015 [13]).
Q5. What is the benchmark for KITTI 2015?
The KITTI 2015 benchmark [13] comprises 200 training and 200 test image pairs (1242 × 375 pixels) with the increasedchallenges of dynamic scene objects (vehicles).
Q6. How is the optical flow recovered from two temporally adjacent images?
Dense optical flow is recovered from two temporally adjacent images (Figure 2A) using a three step process:Point detection on the first image is carried out by calculation of an even grid of DeGraF points [1] shown in Figure 2B.
Q7. What is the definition of dense optical flow?
To cope with such challenges, contemporary optical flow methods use a sparse-to-dense estimation scheme, whereby a sparse set of points on a video frame are matched to points in the subsequent frame.
Q8. Why is the corresponding centroid more robust?
This choice is made because the larger value from Sneg and Spos is less sensitive to noise and so the corresponding centroid is more robust.
Q9. How many pixels are measured in the deGraF-Flow algorithm?
For a predicted optical flow vector up at every pixel with corresponding ground flow truth vector ugt, the EPE is then defined as the average difference between the predicted and ground truth vectors over the image:EPE = 1N ∑ i ‖upi − u gt i ‖ 2, (3)where N is the number of pixels and EPE is hence measured in pixels.
Q10. What is the process of detecting deGraF points in a sequence?
Given two temporally adjacent images in a sequence, DeGraF points are detected in the first image and then efficiently tracked to the subsequent image using RLOF [2].
Q11. What is the percentage of flow vectors with an EPE greater than 3px?
The percentage of flow vectors with an EPE greater than 3px are shown, with fg and bg referring to foreground objects and the background scene respectively.