Unsupervised maritime target detection

The unsupervised detection of maritime targets in grey scale video is a difficult problem in maritime video surveillance. Most approaches assume that the camera is static and employ pixel-wise background modelling techniques for foreground detection; other methods rely on colour or thermal information to detect targets. These methods fail in real-world situations when the static camera assumption is violated, and colour or thermal data is unavailable. In defence and security applications, prior information and training samples of targets may be unavailable for training a classifier; the learning of a one class classifier for the background may be impossible as well. Thus, an unsupervised online approach that attempts to learn from the scene data is highly desirable. In this thesis, the characteristics of the maritime scene and the ocean texture are exploited for foreground detection. Two fast and effective methods are investigated for target detection. Firstly, online regionbased background texture models are explored for describing the appearance of the ocean. This approach avoids the need for frame registration because the model is built spatially rather than temporally. The texture appearance of the ocean is described using Local Binary Pattern (LBP) descriptors. Two models are proposed: one model is a Gaussian Mixture (GMM) and the other, referred to as a Sparse Texture Model (STM), is a set of histogram texture distributions. The foreground detections are optimized using a Graph Cut (GC) that enforces spatial coherence. Secondly, feature tracking is investigated as a means of detecting stable features in an image frame that typically correspond to maritime targets; unstable features are background regions. This approach is a Track-Before-Detect (TBD) concept and it is implemented using a hierarchical scheme for motion estimation, and matching of Scale- Invariant Feature Transform (SIFT) appearance features. The experimental results show that these approaches are feasible for foreground detection in maritime video when the camera is either static or moving. Receiver Operating Characteristic (ROC) curves were generated for five test sequences and the Area Under the ROC Curve (AUC) was analyzed for the performance of the proposed methods. The texture models, without GC optimization, achieved an AUC of 0.85 or greater on four out of the five test videos. At 50% True Positive Rate (TPR), these four test scenarios had a False Positive Rate (FPR) of less than 2%. With the GC optimization, an AUC of greater than 0.8 was achieved for all the test cases and the FPR was reduced in all cases when compared to the results without the GC. In comparison to the state of the art in background modelling for maritime scenes, our texture model methods achieved the best performance or comparable performance. The two texture models executed at a reasonable processing frame rate. The experimental results for TBD show that one may detect target features using a simple track score based on the track length. At 50% TPR a FPR of less than 4% is achieved for four out of the five test scenarios. These results are very promising for maritime target detection

Vision-based adaptive cruise control using pattern matching

Adaptive Cruise Control (ACC) is a relatively new system designed to assist automobile drivers in maintaining a safe following distance. This paper proposes and validates a vision-based ACC system which uses a single camera to obtain the clearance distance between the preceding vehicle and the ACC vehicle. Pattern matching, with the aid of lane detection, is used for vehicle detection. The vehicle and range detection algorithms are validated using real-world data, and then the resulting system performance is shown to be sufficient using a simulation of a basic vehicle model

Influence of using date-specific values when extracting phenological metrics from 8-day composite NDVI data

Information of vegetation dynamics derived from remotely sensed data is essential for regional natural resource management. A common approach involves using an n-day composite time-series of normalized difference vegetation index (NDVI) satellite data for estimating vegetation phenology. The composite method was introduced to deal with problems associated with cloudy and noisy data but could possibly obscure the fine-scale timing of vegetation processes like leaf out and leaf drop, which can occur suddenly, often within a few days. Moreover, different methods for smoothing the NDVI curves also influence the accuracy of the vegetation parameters extracted from them. We investigate the difference between 8-day equalinterval (standard composite data) and 8-day date-specific NDVI data in their ability to extract phenological metrics of interest for ecologists and land managers. We also compare two different filtering algorithms - the Savitsky-Golay and the Gaussian filtering algorithms. Results from a typical savanna system in South Africa show that the Savitsky-Golay technique with date-specific NDVI values is the best method

A three-step vehicle detection framework for range estimation using a single camera

Abstract: This paper proposes and validates a real-time onroad vehicle detection system, which uses a single camera for the purpose of intelligent driver assistance. A three-step vehicle detection framework is presented to detect and track the target vehicle within an image. In the first step, probable vehicle locations are hypothesized using pattern recognition. The vehicle candidates are then verified in the hypothesis verification step. In this step, lane detection is used to filter vehicle candidates that are not within the lane region of interest. In the final step tracking and online learning are implemented to optimize the detection algorithm during misdetection and temporary occlusion. Good detection performance and accuracy was observed in highway driving environments with minimal shadows

Land-cover change in the Kruger to Canyons Biosphere Reserve (1993 - 2006): A first step towards creating a conservation plan for the subregion

This paper is a first step towards a conservation plan for the Kruger to Canyons Biosphere Reserve (K2C) on the South African Central Lowveld, quantifying the historical land-cover trends (1993 - 2006). During the analysis period, 36% of the biosphere reserve (BR) underwent land-cover change. Settlement areas increased by 39.7%, mainly in rural areas, becoming denser, particularly along roadways. Human-Impacted Vegetation increased by 6.8% and Intact Vegetation declined by 7.3%, predominantly around settlement areas, which is testament to the interdependency between rural communities and the local environment. However, settlement expansion exceeded the rate of rangeland growth; in the long term, this may raise questions for sustainable resource extraction. Similarly, the block losses of intact vegetation are of concern; issues of fragmentation arise, with knock-on effects for ecosystem functioning. In the economic sector, agriculture increased by 51.9%, while forestry and mining declined by 7.1% and 6.3%, respectively. The future of these three sectors may also have significant repercussions for land-cover change in the BR. The identification of historical drivers, along with the chance that existing trends may continue, will have important implications for biodiversity protection in this landscape. Applied within a conservation-planning framework, these land-cover data, together with economic and biodiversity data, will help reconcile the spatial requirements of socio-economic development with those of conservation