CamSeq01 Dataset. CamSeq01 is a groundtruth dataset that can be freely used for research work in object recognition in video. This database is unique since it is a video sequence and consists of high resolution images. It includes the original frame sequence and the corresponding labeled frames which constitute the groundtruth. In the labeled frames, each object has been painted with a given class colour by human operators. Applications of this dataset include: object recognition, object label propagation, object tracking ... This dataset has been originally designed for the problem of automated driving vehicle. This sequence depicts a moving driving scene in the city of Cambridge filmed from a moving car. Note: more ground truth datasets similar to this one are currently (as of October 2007) being prepared and will be released soon.
Dataset description list of class labels and corresponding colours The "void" label indicates an area which ambiguous or irrelevant in this context. Image format and naming. CRCV | Center for Research in Computer Vision at the University of Central Florida. Aerobic Actions Recorded from Smartphone Data Set: Click Here Aerobic actions were recorded from subjects using the Inertial Measurement Unit (IMU) on an Apple iPhone 4 smartphone.
The IMU includes a 3D accelerometer, gyroscope, and magnetometer*. Each sample was taken at 60Hz, and manually trimmed to 500 samples (8.33s) to eliminate starting and stopping movements. iPhone is always clipped to the belt on the right hand side as shown in the picture. Note: * Our experiments show that the data from magnetometer is not useful. After feature selection was performed, the most useful features came dominantly from the accelerometer data. Please refer to this publication if you use this data set: Corey McCall, Kishore Reddy and Mubarak Shah, Macro-Class Selection for Hierarchical K-NN Classification of Inertial Sensor Data, Second International Conference on Pervasive and Embedded Computing and Communication Systems, PECCS 2012, February 24-26, 2012, Rome, Italy. PECCS 2012 presentation. Sam roweis : data. AP Statistics Curriculum 2007 IntroVar - Socr.
From Socr General Advance-Placement (AP) Statistics Curriculum - Introduction to Statistics The Nature of Data & Variation No matter how controlled are the environment, the protocol or the design, virtually any repeated measurement, observation, experiment, trial, study or survey is bounded to generate data that varies because of intrinsic (internal to the system) or extrinsic (due to the ambient environment) effects. For example, the UCLA's study of Alzheimer’s disease* analyzed the data of 31 Mild Cognitive Impairment (MCI) and 34 probable Alzheimer’s disease (AD) patients. The investigators made every attempt to control as many variables as possible. Yet, the demographic information they collected from the outcomes of the subjects contained unavoidable variation.
Approach Models and strategies for solving problems and understanding data and inferences. Model Validation Checking/affirming underlying assumptions. Computational Resources: Internet-based SOCR Tools Datasets Examples Problems. Data Sets | GroupLens Research.