This paper presents a novel coastal region detection method for infrared search and track. The coastal region detection is critical to home land security and ship defense. Detected coastal region information can be used to the design of target detector such as moving target detection and threshold setting. We can detect coastal regions robustly by combining the infrared image segmentation and sensor line-of-sight (LOS) information. The K-means-based image segmentation can provide initial region information and the sensor LOS information can predict the approximate horizon location in images. The evidence of coastal region is confirmed by contour extraction results. The experimental results on remote coasts and near coasts validate the robustness of the proposed coastal region detector.
This paper presents a separate spatio-temporal filter based small infrared target detection method to address the sea-based
infrared search and track (IRST) problem in dense sun-glint environment. It is critical to detect small infrared targets such
as sea-skimming missiles or asymmetric small ships for national defense. On the sea surface, sun-glint clutters degrade
the detection performance. Furthermore, if we have to detect true targets using only three images with a low frame rate
camera, then the problem is more difficult. We propose a novel three plot correlation filter and statistics based clutter
reduction method to achieve robust small target detection rate in dense sun-glint environment. We validate the robust
detection performance of the proposed method via real infrared test sequences including synthetic targets.
In this paper, we propose stereo vision-based obstacle detection method on the road using a dense disparity map. We use
the dense disparity map to detect obstacles robustly in real traffic situations. Our method consists of three stages, namely
road feature extraction, column detection, obstacle segmentation. First, we extract a road feature from a v- disparity map
calculated from a dense disparity map. And we perform a column detection using the extracted road feature as a criterion
that decides whether obstacles exist or not. Finally, we perform a segmentation using a bird's-eye view mapping to
divide the merged obstacle into each obstacle accurately. We conduct experiments to verify our method in the real traffic
situations.
In this paper, we present a multi-vehicle tracking method that uses integrated position and motion tracking methods to minimize missing and false detection. No existing state-of-the-art vehicle detection method can detect all the vehicles on the road and remove all false positive alarms. Therefore, a robust tracking-by-detection algorithm is necessary to minimize the number of false positive and false negative alarms. In multi-vehicle tracking, there are three types of errors such as false negative alarms, false positive alarms, and track identity switches. False negative and false positive alarms are caused by an imperfect detection algorithm, while track identity switches are caused by measurement-to-track pair confusion. Our tracking-by-detection method minimizes these errors while processing in real-time for online application. Sparse false positive alarms are reduced by a track initialization procedure. Motion tracking with selected features can minimize false negative alarms. A data association algorithm with complementary global and local distance prevents tracks from connecting measurements incorrectly. The proposed method was evaluated and verified in challenging, real road environments. The experimental results demonstrate that our multi-vehicle tracking method remarkably reduces false positive and false negative alarms and performs better than previous methods.
This paper presents stereo vision-based vehicle detection approach on the road using a road feature and disparity histogram. It is not easy to detect only vehicles robustly on the road in various traffic situations, for example, a nonflat road or a multiple-obstacle situation. This paper focuses on the improvement of vehicle detection performance in various real traffic situations. The approach consists of three steps, namely obstacle localization, obstacle segmentation, and vehicle verification. First, we extract a road feature from v-disparity maps binarized using the most frequent values in each row and column, and adopt the extracted road feature as an obstacle criterion in column detection. However, many obstacles still coexist in each localized obstacle area. Thus, we divide the localized obstacle area into multiple obstacles using a disparity histogram and remerge the divided obstacles using four criteria parameters, namely the obstacle size, distance, and angle between the divided obstacles, and the difference of disparity values. Finally, we verify the vehicles using a depth map and gray image to improve the performance. We verify the performance of our proposed method by conducting experiments in various real traffic situations. The average recall rate of vehicle detection is 95.5%.
In this paper, we present a visual obstacle detection and tracking system based on a dense stereo vision method. We
combine a global stereo matcher with a correlation based cost function for generating a reliable disparity-map. An NCC
algorithm is robust to illumination variation, and a BP based global disparity computation algorithm is efficient for
recovering the disparity information of a large textureless area in real driving scenes. Then an obstacle detector and a
tracker module are implemented and tested under actual driving conditions. Using U-V disparity representation, a road
profile is efficiently extracted, and obstacle ROI can be detected. In the process of obstacle detection, a few heuristic
constraints are applied to exclude wrong candidates, and a further verification step is proceeded by a tracker.
Implemented system offers accurate and reliable range images under various noisy imaging conditions, which results in
robust detection and tracking performance.
In this paper, we present a low memory-cost message iteration architecture for a fast belief propagation(BP) algorithm.
To meet the real-time goal, our architecture basically follows multi-scale BP method and truncated linear smoothness
cost model. We observe that the message iteration process in BP requires a huge intermediate buffer to store four
directional messages of the whole node. Therefore, instead of updating all the node messages in each iteration sequence,
we propose that individual node could be completed iteration process in ahead and consecutively execute it node by
node. The key ideas in this paper focus on both maximizing architecture's parallelism and minimizing implementation
cost overhead. Therefore, we first apply a pipelined architecture to each iteration stage that is executed independently.
Note that pipelining makes it faster message throughput at a single iteration cycle rather than consuming whole iteration
cycle time as previously. We also make multiple message update nodes as a minimal processing unit to maximize the
parallelism. For the multi-scale BP method, the proposed parallel architecture does not cause additional execution time
for processing the nodes in the down-scaled Markov Random Field(MRF). Considering VGA image size, 4 iterations per
each scale and 64 disparity levels, our approach can reduce memory complexity by 99.7% and make it 340 times faster
than the general multi-scale BP architecture.
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