Vehicle Detection and Classification for Cluttered Urban Intersection

Object detection and classification from large-scale cluttered indoor scans

We present a method to automatically segment indoor scenes by detecting repeated objects. Our algorithm scales to datasets with 198 million points and does not require any training data. We propose a trivially parallelizable preprocessing step, which compresses a point cloud into a collection of nearly-planar patches related by geometric transformations. This representation enables us to robust...

Automatic Aircraft Detection and Classification in Cluttered Environments

A vision-based system for detecting and classifying moving obstacles in a cluttered environment using a single camera mounted on a small unmanned aerial vehicle is presented. Feature correspondences between successive image frames are found using the SIFT algorithm, and a model of the background motion is generated by Random Sample Consensus (RANSAC). The transformed frames are then differenced...

Detection, Classification, & Identification of Objects in Cluttered Images

Model based vehicle detection and tracking for autonomous urban driving

Situational awareness is crucial for autonomous driving in urban environments. This paper describes the moving vehicle detection and tracking module that we developed for our autonomous driving robot Junior. The robot won second place in the Urban Grand Challenge, an autonomous driving race organized by the U.S. Government in 2007. The module provides reliable detection and tracking of moving v...

Morphological Process based Vehicle Detection and Classification

Vehicle detection and classification of vehicles play an important role in decision making for the purpose of traffic control and management. We propose a novel and efficient algorithm based on image processing using vertically positioned camera for vehicle detection and classification according to their size. The algorithm is based on different techniques including image differencing, thrshold...