fly的coco

第 19 章 基于语音识别的信号灯图像模拟控制技术

基于特征匹配的英文印刷字符识别

第 23 章 基于光流场的交通汽车检测跟踪

yolov5-v6.1.zip

sharingcode-LCKSVD,分类

analysis KSVD,系数表示中和ksvd对应的一种稀疏表示方法

Fast Subspace Clustering via Sparse Representations 利用系数表示做快速子空间聚类matlabd代码 优于SSC

Fast Subspace Clustering via Sparse Representations 利用系数表示做快速子空间聚类论文 优于SSC

subspace clustering 数据降维，自空间聚类，优于Kmean等聚类算法

image super resolution for sparse representation matlab coding

image fusion matlab toolbox

In this paper, we address the issue of tracking moving objects in an environment covered by multiple uncalibrated cameras with overlapping fields of view, typical of most surveillance setups. In such a scenario, it is essential to establish correspondence between tracks of the same object, seen in different cameras, to recover complete information about the object. We call this the problem of consistent labeling of objects when seen in multiple cameras. We employ a novel approach of finding the limits of field of view (FOV) of each camera as visible in the other cameras. We show that if the FOV lines are known, it is possible to disambiguate between multiple possibilities for correspondence. We present a method to automatically recover these lines by observing motion in the environment. Furthermore, once these lines are initialized, the homography between the views can also be recovered. We present results on indoor and outdoor sequences, containing persons and vehicles.

We present a multi-camera system based on Bayesian modality fusion to track multiple people in an indoor environment. Bayesian networks are used to combine multiple modalities for matching subjects between consecutive imge frames and between multiple camera views. Unlike other occlusion reasoning methods, we use multiple cameras in order to obtain continuous visual information of people in either or both cameras so that they can be tracked through interactions. Results demonstrate that the system can maintain people’s identities by using multiple cameras cooperatively.

Abstract—In video surveillance, detection of moving objects from an image sequence is very important for target tracking, activity recognition, and behavior understanding. Background subtraction is a very popular approach for foreground segmentation in a still scene image. In order to compensate for illumination changes, a background model updating process is generally adopted, and leads to extra computation time. In this paper, we propose a fast background subtraction scheme using independent component analysis (ICA) and, particularly, aims at indoor surveillance for possible applications in home-care and health-care monitoring, where moving and motionless persons must be reliably detected. The proposed method is as computationally fast as the simple image difference method, and yet is highly tolerable to changes in room lighting. The proposed background subtraction scheme involves two stages, one for training and the other for detection. In the training stage, an ICA model that directly measures the statistical independency based on the estimations of joint and marginal probability density functions from relative frequency distributions is first proposed. The proposed ICA model can well separate two highly-correlated images. In the detection stage, the trained de-mixing vector is used to separate the foreground in a scene image with respect to the reference background image. Two sets of indoor examples that involve switching on/off room lights and opening/closing a door are demonstrated in the experiments. The performance of the proposed ICA model for background subtraction is also compared with that of the well-known FastICA algorithm.