Data Mining - Concepts and Techniques.

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

We propose a lightweight and ground-optimized lidar odometry and mapping method, LeGO-LOAM, for realtime six degree-of-freedom pose estimation with ground vehicles. LeGO-LOAM is lightweight, as it can achieve realtime pose estimation on a low-power embedded system. LeGO-LOAM is ground-optimized, as it leverages the presence of a ground plane in its segmentation and optimization steps. We first apply point cloud segmentation to filter out noise, and feature extraction to obtain distinctive planar and edge features. A two-step Levenberg-Marquardt optimization method then uses the planar and edge features to solve different components of the six degree-of-freedom transformation across consecutive scans. We compare the performance of LeGO-LOAM with a state-of-the-art method, LOAM, using datasets gathered from variable-terrain environments with ground vehicles, and show that LeGO-LOAM achieves similar or better accuracy with reduced computational expense. We also integrate LeGO-LOAM into a SLAM framework to eliminate the pose estimation error caused by drift, which is tested using the KITTI dataset.

LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

Video indexing and retrieval have a wide spectrum of promising applications, motivating the interest of researchers worldwide. This paper offers a tutorial and an overview of the landscape of general strategies in visual content-based video indexing and retrieval, focusing on methods for video structure analysis, including shot boundary detection, key frame extraction and scene segmentation, extraction of features including static key frame features, object features and motion features, video data mining, video annotation, video retrieval including query interfaces, similarity measure and relevance feedback, and video browsing. Finally, we analyze future research directions.

A Survey on Visual Content-Based Video Indexing and Retrieval

We are dealing with the problem of fine-grained vehicle make&model recognition and verification. Our contribution is showing that extracting additional data from the video stream – besides the vehicle image itself – and feeding it into the deep convolutional neural network boosts the recognition performance considerably. This additional information includes: 3D vehicle bounding box used for "unpacking" the vehicle image, its rasterized low-resolution shape, and information about the 3D vehicle orientation. Experiments show that adding such information decreases classification error by 26% (the accuracy is improved from 0.772 to 0.832) and boosts verification average precision by 208% (0.378 to 0.785) compared to baseline pure CNN without any input modifications. Also, the pure baseline CNN outperforms the recent state of the art solution by 0.081. We provide an annotated set "BoxCars" of surveillance vehicle images augmented by various automatically extracted auxiliary information. Our approach and the dataset can considerably improve the performance of traffic surveillance systems.

/pdf/boxcars-3d-boxes-as-cnn-input-for-improved-fine-grained-1qm2f4g0tr.pdf

BoxCars: 3D Boxes as CNN Input for Improved Fine-Grained Vehicle Recognition

This paper proposes an approach to the vehicle reidentification problem in a multiple camera system. We focused on the re-identification itself assuming that the vehicle detection problem is already solved including extraction of a full-fledged 3D bounding box. The re-identification problem is solved by using color histograms and histograms of oriented gradients by a linear regressor. The features are used in separate models in order to get the best results in the shortest CPU computation time. The proposed method works with a high accuracy (60% true positives retrieved with 10% false positive rate on a challenging subset of the test data) in 85 milliseconds of the CPU (Core i7) computation time per one vehicle re-identification assuming the fullHD resolution video input. The applications of this work include finding important parameters such as travel time, traffic flow, or traffic information in a distributed traffic surveillance and monitoring system.

/pdf/vehicle-re-identification-for-automatic-video-traffic-jghayl2pvm.pdf

Vehicle Re-identification for Automatic Video Traffic Surveillance

In this paper, we focus on fine-grained recognition of vehicles mainly in traffic surveillance applications. We propose an approach that is orthogonal to recent advancements in fine-grained recognition (automatic part discovery and bilinear pooling). In addition, in contrast to other methods focused on fine-grained recognition of vehicles, we do not limit ourselves to a frontal/rear viewpoint, but allow the vehicles to be seen from any viewpoint. Our approach is based on 3-D bounding boxes built around the vehicles. The bounding box can be automatically constructed from traffic surveillance data. For scenarios where it is not possible to use precise construction, we propose a method for an estimation of the 3-D bounding box. The 3-D bounding box is used to normalize the image viewpoint by “unpacking” the image into a plane. We also propose to randomly alter the color of the image and add a rectangle with random noise to a random position in the image during the training of convolutional neural networks (CNNs). We have collected a large fine-grained vehicle data set BoxCars116k, with 116k images of vehicles from various viewpoints taken by numerous surveillance cameras. We performed a number of experiments, which show that our proposed method significantly improves CNN classification accuracy (the accuracy is increased by up to 12% points and the error is reduced by up to 50% compared with CNNs without the proposed modifications). We also show that our method outperforms the state-of-the-art methods for fine-grained recognition.

BoxCars: Improving Fine-Grained Recognition of Vehicles Using 3-D Bounding Boxes in Traffic Surveillance

Calibration of the LiDAR sensor with RGB camera finds its usage in many application fields from enhancing
image classification to the environment perception and mapping. This paper presents a pipeline for mutual pose
and orientation estimation of the mentioned sensors using a coarse to fine approach. Previously published methods
use multiple views of a known chessboard marker for computing the calibration parameters, or they are limited to
the calibration of the sensors with a small mutual displacement only. Our approach presents a novel 3D marker for
coarse calibration which can be robustly detected in both the camera image and the LiDAR scan. It also requires
only a single pair of camera-LiDAR frames for estimating large sensors displacement. Consequent refinement step
searches for more accurate calibration in small subspace of calibration parameters. The paper also presents a novel
way for evaluation of the calibration precision using projection error.

/pdf/calibration-of-rgb-camera-with-velodyne-lidar-38s045nzj2.pdf

Calibration of RGB camera with velodyne LiDAR

We propose a method for fully automatic calibration of traffic surveillance cameras. This method allows for calibration of the camera – including scale – without any user input, only from several minutes of input surveillance video. The targeted applications include speed measurement, measurement of vehicle dimensions, vehicle classification, etc. The first step of our approach is camera calibration by determining three vanishing points defining the stream of vehicles. The second step is construction of 3D bounding boxes of individual vehicles and their measurement up to scale. We propose to first construct the projection of the bounding boxes and then, by using the camera calibration obtained earlier, create their 3D representation. In the third step, we use the dimensions of the 3D bounding boxes for calibration of the scene scale. We collected a dataset with ground truth speed and distance measurements and evaluate our approach on it. The achieved mean accuracy of speed and distance measurement is below 2%. Our efficient C++ implementation runs in real time on a low-end processor (Core i3) with a safe margin even for full-HD videos.

/pdf/automatic-camera-calibration-for-traffic-understanding-45h4yo4an2.pdf

Adam Herout

Papers

BoxCars: 3D Boxes as CNN Input for Improved Fine-Grained Vehicle Recognition

Vehicle Re-identification for Automatic Video Traffic Surveillance

BoxCars: Improving Fine-Grained Recognition of Vehicles Using 3-D Bounding Boxes in Traffic Surveillance

Calibration of RGB camera with velodyne LiDAR

Automatic Camera Calibration for Traffic Understanding.