The quantitative evaluation of optical flow algorithms by Barron et al. (1994) led to significant advances in performance. The challenges for optical flow algorithms today go beyond the datasets and evaluation methods proposed in that paper. Instead, they center on problems associated with complex natural scenes, including nonrigid motion, real sensor noise, and motion discontinuities. We propose a new set of benchmarks and evaluation methods for the next generation of optical flow algorithms. To that end, we contribute four types of data to test different aspects of optical flow algorithms: (1) sequences with nonrigid motion where the ground-truth flow is determined by tracking hidden fluorescent texture, (2) realistic synthetic sequences, (3) high frame-rate video used to study interpolation error, and (4) modified stereo sequences of static scenes. In addition to the average angular error used by Barron et al., we compute the absolute flow endpoint error, measures for frame interpolation error, improved statistics, and results at motion discontinuities and in textureless regions. In October 2007, we published the performance of several well-known methods on a preliminary version of our data to establish the current state of the art. We also made the data freely available on the web at http://vision.middlebury.edu/flow/ . Subsequently a number of researchers have uploaded their results to our website and published papers using the data. A significant improvement in performance has already been achieved. In this paper we analyze the results obtained to date and draw a large number of conclusions from them.

/pdf/a-database-and-evaluation-methodology-for-optical-flow-2numl3kx6v.pdf

A Database and Evaluation Methodology for Optical Flow

IEEE Transactions on Pattern Analysis and Machine Intelligence

Detecting humans in films and videos is a challenging problem owing to the motion of the subjects, the camera and the background and to variations in pose, appearance, clothing, illumination and background clutter. We develop a detector for standing and moving people in videos with possibly moving cameras and backgrounds, testing several different motion coding schemes and showing empirically that orientated histograms of differential optical flow give the best overall performance. These motion-based descriptors are combined with our Histogram of Oriented Gradient appearance descriptors. The resulting detector is tested on several databases including a challenging test set taken from feature films and containing wide ranges of pose, motion and background variations, including moving cameras and backgrounds. We validate our results on two challenging test sets containing more than 4400 human examples. The combined detector reduces the false alarm rate by a factor of 10 relative to the best appearance-based detector, for example giving false alarm rates of 1 per 20,000 windows tested at 8% miss rate on our Test Set 1.

/pdf/human-detection-using-oriented-histograms-of-flow-and-4y62o7njxp.pdf

Human detection using oriented histograms of flow and appearance

Ground truth optical flow is difficult to measure in real scenes with natural motion As a result, optical flow data sets are restricted in terms of size, complexity, and diversity, making optical flow algorithms difficult to train and test on realistic data We introduce a new optical flow data set derived from the open source 3D animated short film Sintel This data set has important features not present in the popular Middlebury flow evaluation: long sequences, large motions, specular reflections, motion blur, defocus blur, and atmospheric effects Because the graphics data that generated the movie is open source, we are able to render scenes under conditions of varying complexity to evaluate where existing flow algorithms fail We evaluate several recent optical flow algorithms and find that current highly-ranked methods on the Middlebury evaluation have difficulty with this more complex data set suggesting further research on optical flow estimation is needed To validate the use of synthetic data, we compare the image- and flow-statistics of Sintel to those of real films and videos and show that they are similar The data set, metrics, and evaluation website are publicly available

/pdf/a-naturalistic-open-source-movie-for-optical-flow-evaluation-xaa5syym2h.pdf

A naturalistic open source movie for optical flow evaluation

Recent progress in computer vision has been driven by high-capacity models trained on large datasets. Unfortunately, creating large datasets with pixel-level labels has been extremely costly due to the amount of human effort required. In this paper, we present an approach to rapidly creating pixel-accurate semantic label maps for images extracted from modern computer games. Although the source code and the internal operation of commercial games are inaccessible, we show that associations between image patches can be reconstructed from the communication between the game and the graphics hardware. This enables rapid propagation of semantic labels within and across images synthesized by the game, with no access to the source code or the content. We validate the presented approach by producing dense pixel-level semantic annotations for 25 thousand images synthesized by a photorealistic open-world computer game. Experiments on semantic segmentation datasets show that using the acquired data to supplement real-world images significantly increases accuracy and that the acquired data enables reducing the amount of hand-labeled real-world data: models trained with game data and just \(\tfrac{1}{3}\) of the CamVid training set outperform models trained on the complete CamVid training set.

/pdf/playing-for-data-ground-truth-from-computer-games-3yw6rmy1z0.pdf

Playing for Data: Ground Truth from Computer Games

http://www.cs.otago.ac.nz/staffpriv/mccane/publications/benchmarking_optical_flow.pdf

On Benchmarking Optical Flow

Evaluating the performance of optical flow algorithms has been difficult because of the lack of ground-truth data sets for complex scenes We describe a simple modification to a ray tracer that allows us to generate ground-truth motion fields for scenes of arbitrary complexity The resulting flow maps are used to assist in the comparison of eight optical flow algorithms using three complex, synthetic scenes Our study found that a modified version of Lucas and Kanade’s algorithm has superior performance but produces sparse flow maps Proesmans et al’s algorithm performs slightly worse, on average, but produces a very dense depth map

/pdf/recovering-motion-fields-an-evaluation-of-eight-optical-flow-3xlfqyexuc.pdf

Recovering Motion Fields: An Evaluation of Eight Optical Flow Algorithms

Scene classification in indoor and outdoor environments is a fundamental problem to the vision and robotics community. Scene classification benefits from image features which are invariant to image transformations such as rotation, illumination, scale, viewpoint, noise etc. Selecting suitable features that exhibit such invariances plays a key part in classification performance. This paper summarizes the performance of two robust feature detection algorithms namely Scale Invariant Feature Transform (SIFT) and Speeded up Robust Features (SURF) on several classification datasets. In this paper, we have proposed three shorter SIFT descriptors. Results show that the proposed 64D and 96D SIFT descriptors perform as well as traditional 128D SIFT descriptors for image matching at a significantly reduced computational cost. SURF has also been observed to give good classification results on different datasets.

/pdf/sift-and-surf-performance-evaluation-against-various-image-464y1vxjb3.pdf

SIFT and SURF Performance Evaluation against Various Image Deformations on Benchmark Dataset

Background
Musculoskeletal physiotherapists routinely assess lumbar segmental motion during the clinical examination of a patient with low back pain. The validity of manual assessment of segmental motion has not, however, been adequately investigated.

/pdf/lumbar-segmental-instability-a-criterion-related-validity-5en6ybgjdf.pdf

Lumbar segmental instability: a criterion-related validity study of manual therapy assessment

This letter introduces the LOOP binary descriptor (local optimal-oriented pattern) that encodes rotation invariance into the main formulation itself. This makes any post processing stage for rotation invariance redundant and improves on both accuracy and time complexity. We consider fine-grained lepidoptera (moth/butterfly) species recognition as the representative problem since it involves repetition of localized patterns and textures that may be exploited for discrimination. We evaluate the performance of LOOP against its predecessors as well as few other popular descriptors. Besides experiments on standard benchmarks, we also introduce a new small image dataset on NZ Lepidoptera. LOOP performs as well or better on all datasets evaluated compared to previous binary descriptors. The new dataset and demo code of the proposed method are available through the lead author's academic webpage and GitHub.

Brendan McCane

Papers

On Benchmarking Optical Flow

Recovering Motion Fields: An Evaluation of Eight Optical Flow Algorithms

SIFT and SURF Performance Evaluation against Various Image Deformations on Benchmark Dataset

Lumbar segmental instability: a criterion-related validity study of manual therapy assessment

LOOP Descriptor: Local Optimal-Oriented Pattern