“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

This paper investigates long-term tracking of unknown objects in a video stream. The object is defined by its location and extent in a single frame. In every frame that follows, the task is to determine the object's location and extent or indicate that the object is not present. We propose a novel tracking framework (TLD) that explicitly decomposes the long-term tracking task into tracking, learning, and detection. The tracker follows the object from frame to frame. The detector localizes all appearances that have been observed so far and corrects the tracker if necessary. The learning estimates the detector's errors and updates it to avoid these errors in the future. We study how to identify the detector's errors and learn from them. We develop a novel learning method (P-N learning) which estimates the errors by a pair of “experts”: (1) P-expert estimates missed detections, and (2) N-expert estimates false alarms. The learning process is modeled as a discrete dynamical system and the conditions under which the learning guarantees improvement are found. We describe our real-time implementation of the TLD framework and the P-N learning. We carry out an extensive quantitative evaluation which shows a significant improvement over state-of-the-art approaches.

Tracking-Learning-Detection

The PASCAL Visual Object Classes Challenge

Many computer vision problems (e.g., camera calibration, image alignment, structure from motion) are solved through a nonlinear optimization method. It is generally accepted that 2nd order descent methods are the most robust, fast and reliable approaches for nonlinear optimization of a general smooth function. However, in the context of computer vision, 2nd order descent methods have two main drawbacks: (1) The function might not be analytically differentiable and numerical approximations are impractical. (2) The Hessian might be large and not positive definite. To address these issues, this paper proposes a Supervised Descent Method (SDM) for minimizing a Non-linear Least Squares (NLS) function. During training, the SDM learns a sequence of descent directions that minimizes the mean of NLS functions sampled at different points. In testing, SDM minimizes the NLS objective using the learned descent directions without computing the Jacobian nor the Hessian. We illustrate the benefits of our approach in synthetic and real examples, and show how SDM achieves state-of-the-art performance in the problem of facial feature detection. The code is available at www.humansensing.cs. cmu.edu/intraface.

/pdf/supervised-descent-method-and-its-applications-to-face-assiism8su.pdf

Supervised Descent Method and Its Applications to Face Alignment

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

Several applications require information about street furniture. Part of the task is to survey all traffic signs. This has to be done for millions of km of road, and the exercise needs to be repeated every so often. We used a van with eight roof-mounted cameras to drive through the streets and took images every meter. The paper proposes a pipeline for the efficient detection and recognition of traffic signs from such images. The task is challenging, as illumination conditions change regularly, occlusions are frequent, sign positions and orientations vary substantially, and the actual signs are far less similar among equal types than one might expect. We combine 2D and 3D techniques to improve results beyond the state-of-the-art, which is still very much preoccupied with single view analysis. For the initial detection in single frames, we use a set of colour- and shape-based criteria. They yield a set of candidate sign patterns. The selection of such candidates allows for a significant speed up over a sliding window approach while keeping similar performance. A speedup is also achieved through a proposed efficient bounded evaluation of AdaBoost detectors. The 2D detections in multiple views are subsequently combined to generate 3D hypotheses. A Minimum Description Length formulation yields the set of 3D traffic signs that best explains the 2D detections. The paper comes with a publicly available database, with more than 13,000 traffic signs annotations.

Multi-view traffic sign detection, recognition, and 3D localisation

Several applications require information about street furniture. Part of the task is to survey all traffic signs. This has to be done for millions of km of road, and the exercise needs to be repeated every so often. A van with 8 roof-mounted cameras drove through the streets and took images every meter. The paper proposes a pipeline for the efficient detection and recognition of traffic signs. The task is challenging, as illumination conditions change regularly, occlusions are frequent, 3D positions and orientations vary substantially, and the actual signs are far less similar among equal types than one might expect. We combine 2D and 3D techniques to improve results beyond the state-of-the-art, which is still very much preoccupied with single view analysis.

/pdf/multi-view-traffic-sign-detection-recognition-and-3d-4fd4z69oqz.pdf

Licence plates and traffic signs detection and recognition have a number of different applications relevant for transportation systems, such as traffic monitoring, detection of stolen vehicles, driver navigation support or any statistical research. A number of methods have been proposed, but only for particular cases and working under constraints (e.g. known text direction or high resolution). Therefore a new class of locally threshold separable detectors based on extremal regions, which can be adapted by machine learning techniques to arbitrary shapes, is proposed. In the test set of licence plate images taken from different viewpoints (-45/spl deg/,45/spl deg/), scales (from seven to hundreds of pixels height) even in bad illumination conditions and partial occlusions, the high detection accuracy is achieved (95%). Finally we present the detector generic abilities by traffic signs detection. The standard classifier (neural network) within the detector selects a relevant subset of extremal regions, i.e. regions that are connected components of a thresholded image. Properties of extremal regions render the detector very robust to illumination change and partial occlusions. Robustness to a viewpoint change is achieved by using invariant descriptors and/or by modelling shape variations by the classifier. The time-complexity of the detection is approximately linear in the number of pixel and a non-optimized implementation runs at about 1 frame per second for a 640 /spl times/ 480 image on a high-end PC.

/pdf/unconstrained-licence-plate-and-text-localization-and-27heahc7nq.pdf

Unconstrained licence plate and text localization and recognition

We propose a learning approach to tracking explicitly minimizing the computational complexity of the tracking process subject to user-defined probability of failure (loss-of-lock) and precision. The tracker is formed by a Number of Sequences of Learned Linear Predictors (NoSLLiP). Robustness of NoSLLiP is achieved by modeling the object as a collection of local motion predictors - object motion is estimated by the outlier-tolerant RANSAC algorithm from local predictions. Efficiency of the NoSLLiP tracker stems from (i) the simplicity of the local predictors and (ii) from the fact that all design decisions - the number of local predictors used by the tracker, their computational complexity (i.e. the number of observations the prediction is based on), locations as well as the number of RANSAC iterations are all subject to the optimization (learning) process. All time-consuming operations are performed during the learning stage - tracking is reduced to only a few hundreds integer multiplications in each step. On PC with 1timesK8 3200+, a predictor evaluation requires about 30 mus. The proposed approach is verified on publicly-available sequences with approximately 12000 frames with ground-truth. Experiments demonstrates, superiority in frame rates and robustness with respect to the SIFT detector, Lucas-Kanade tracker and other trackers.

/pdf/tracking-by-an-optimal-sequence-of-linear-predictors-4t6w3widp9.pdf

Tracking by an Optimal Sequence of Linear Predictors

The Subterranean Challenge (SubT) is a contest organised by the Defense Advanced Research Projects Agency (DARPA). The contest reflects the requirement of increasing safety and efficiency of underground search-and-rescue missions. In the SubT challenge, teams of mobile robots have to detect, localise and report positions of specific objects in an underground environment. This paper provides a description of the multi-robot heterogeneous exploration system of our CTU-CRAS team, which scored third place in the Tunnel Circuit round, surpassing the performance of all other non-DARPA-funded competitors. In addition to the description of the platforms, algorithms and strategies used, we also discuss the lessons-learned by participating at such contest.

Karel Zimmermann

Papers

Multi-view traffic sign detection, recognition, and 3D localisation

Multi-view traffic sign detection, recognition, and 3D localisation

Unconstrained licence plate and text localization and recognition

Tracking by an Optimal Sequence of Linear Predictors

DARPA Subterranean Challenge: Multi-robotic Exploration of Underground Environments