Performance characterization of the dynamic programming obstacle detection algorithm

Abstract

A computer vision-based system using images from an airborne aircraft can increase flight safety by aiding the pilot to detect obstacles in the flight path so as to avoid mid-air collisions. Such a system fits naturally with the development of an external vision system proposed by NASA for use in high-speed civil transport aircraft with limited cockpit visibility. The detection techniques should provide high detection probability for obstacles that can vary from subpixels to a few pixels in size, while maintaining a low false alarm probability in the presence of noise and severe background clutter. Furthermore, the detection algorithms must be able to report such obstacles in a timely fashion, imposing severe constraints on their execution time. For this purpose, we have implemented a number of algorithms to detect airborne obstacles using image sequences obtained from a camera mounted on an aircraft. This paper describes the methodology used for characterizing the performance of the dynamic programming obstacle detection algorithm and its special cases. The experimental results were obtained using several types of image sequences, with simulated and real backgrounds. The approximate performance of the algorithm is also theoretically derived using principles of statistical analysis in terms of the signal-to-noise ration (SNR) required for the probabilities of false alarms and misdetections to be lower than prespecified values. The theoretical and experimental performance are compared in terms of the required SNR.

Airborne vision‐based collision‐detection system

“…Over the past three decades, a two‐stage processing paradigm has emerged for the simultaneous detection and tracking of dim, subpixel‐sized targets (Arnold, Shaw, & Pasternack, 1993; Barniv, 1985; Gandhi, Yang, Kasturi, Camps, Coraor, et al, 2003, 2006). These two stages are (1) an image preprocessing stage that, within each frame, highlights potential targets with attributes of interest and (2) a subsequent temporal filtering stage that exploits target dynamics across frames.…”

“…Specific implementations of the morphological filtering approach include the hit‐or‐miss filter (Schaefer & Casasent, 1995), close‐minus‐open filter (Casasent & Ye, 1997), and top‐hat filter (Braga‐Neto, Choudhary, & Goutsias, 2004). Although a large proportion of research has focused on IR images, there are recent examples of morphological filters being incorporated into target detection algorithms operating on visual spectrum images (Carnie, Walker, & Corke, 2006; Gandhi et al, 2003, 2006). Moreover, a sign of the increasing popularity of morphological filters for small target detection is evident in the host of studies undertaken into the issue of parameter design (Yu, Wu, Wu, & Li, 2003; Zeng, Li, & Peng, 2006).…”

“…Moreover, a sign of the increasing popularity of morphological filters for small target detection is evident in the host of studies undertaken into the issue of parameter design (Yu, Wu, Wu, & Li, 2003; Zeng, Li, & Peng, 2006). Finally, efforts have been made to compare existing techniques with the morphology‐based filters (Barnett, Billard, & Lee, 1993; Gandhi et al, 2006; Tom et al, 1993; Warren, 2002), with the median filtering technique often featuring in the comparison studies.…”

Evaluation of Maritime Vision Techniques for Aerial Search of Humans in Maritime Environments

“…Gandi [13] and Carnie [9] applied DP to the image based target detection/tracking problem to optimise the target classification decisions at each frame (i.e. stage) of the process while using relatively simple constraints.…”

Learning to Detect Aircraft at Low Resolutions

“…However, this approach generates a significant number of false positives and requires tracking of the features over a large number of frames as reported in [7], [8].…”