Fast Vehicle Localisation and Recognition Without Line Extraction and Matching

A novel algorithm is presented in this paper for vehicle localisation and recognition under the ground-plane constraint. Unlike the vast majority of the existing modelbased object recognition schemes, the algorithm eliminates the need for explicit feature extraction and matching so that the computational cost is substantially lower. The algorithm is tested extensively with routine outdoor traffic images. Experimental results are included to illustrate the performance of the algorithm. The algorithm is developed for real-time implementation for applications in traffic scene analysis. It may readily be adapted to other industrial applications.

Identification and tracking of marine objects for collision risk estimation.

With the advent of modem high-speed passenger ferries and the general increase in maritime traffic, both commercial and recreational, marine safety is becoming an increasingly important issue. From lightweight catamarans and fishing trawlers to container ships and cruise liners one question remains the same. Is anything in the way? This question is addressed in this thesis. Through the use of image processing techniques applied to video sequences of maritime scenes the images are segmented into two regions, sea and object. This is achieved using statistical measures taken from the histogram data of the images. Each segmented object has a feature vector built containing information including its size and previous centroid positions. The feature vectors are used to track the identified objects across many frames. With information recorded about an object's previous motion its future motion is predicted using a least squares method. Finally a high-level rule-based algorithm is applied in order to estimate the collision risk posed by each object present in the image. The result is an image with the objects identified by the placing of a white box around them. The predicted motion is shown and the estimated collision risk posed by that object is displayed. The algorithms developed in this work have been evaluated using two previously unseen maritime image sequences. These show that the algorithms developed here can be used to estimate the collision risk posed by maritime objects

Target tracking and image interpretation in natural open world scenes

This thesis is concerned with tracking man made objects moving in natural open world scenes and based on the tracking data, construct a structural representation of that scene, frame by frame. The system developed uses a static camera and a statistical frame differencing technique for detecting motion in an image that has a relatively static background. Objects with a measured temporal consistency are tracked across successive image frames. Based on the tracking data, regions in the scene are associated with particular types of dynamic event. For example regions containing movement (could be roads) and regions where objects seem to disappear or partially disappear (could be hedges). Because of the sensitivity of the motion estimator to changes in scene illumination and environmental conditions, a tile-based method is used to detect scene motion based on the estimations of statistical variations within the tiles. An updating process is used to ensure that a reliable estimate of the background reference image is maintained by the system. Motion cues are matched against tracked objects from a previous frame using an estimate of the temporal continuity of an object. A spatial-temporal reasoning process is used to infer the structure in the image. This inference mechanism is implemented using a semantic network. The system has been tested on several open world sequences and in each case has demonstrated that it can identify and track vehicles moving in the scene. Based on the motion of these vehicles regions in the image were identified and scene maps constructed for each scene. The map identified regions where vehicles can be expected to be observed moving and regions where they could become occluded. A CD-ROM is included with this thesis that contains the results obtained by the system for the two image sequences used in chapter seven. These results incorporate some of the enhancements outlined in chapter 8, section 8.3. A windows movie player is included on the CD-ROM and appendix d provides information on the contents of the CD-ROM together with installation and operating instructions

Identification and tracking of maritime objects for collision risk estimation

With the advent of modem high-speed passenger ferries and the general increase in maritime traffic, both commercial and recreational, marine safety is becoming an increasingly important issue. From lightweight catamarans and fishing trawlers to container ships and cruise liners one question remains the same. Is anything in the way? This question is addressed in this thesis. Through the use of image processing techniques applied to video sequences of maritime scenes the images are segmented into two regions, sea and object. This is achieved using statistical measures taken from the histogram data of the images. Each segmented object has a feature vector built containing information including its size and previous centroid positions. The feature vectors are used to track the identified objects across many frames. With information recorded about an object's previous motion its future motion is predicted using a least squares method. Finally a high-level rule-based algorithm is applied in order to estimate the collision risk posed by each object present in the image. The result is an image with the objects identified by the placing of a white box around them. The predicted motion is shown and the estimated collision risk posed by that object is displayed. The algorithms developed in this work have been evaluated using two previously unseen maritime image sequences. These show that the algorithms developed here can be used to estimate the collision risk posed by maritime objects.EThOS - Electronic Theses Online ServiceGBUnited Kingdo