Evaluation of Motion Segmentation Quality for Aircraft Activity Surveillance

Aguilera, Josep; Wildenauer, Horst; Kampel, Martin; Borg, Mark G.; Thirde, D.; Ferryman, James

doi:10.1109/vspets.2005.1570928

Cited by 28 publications

(23 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The performance evaluation of the different motion detector algorithms for AVITRACK is described in more detail in [1]. It is noted that some objects are partially detected due to the achromaticity of the scene and the presence of fog causes a relatively high number of foreground pixels to be misclassified as highlighted background pixels resulting in a decrease in accuracy.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Automated Scene Understanding for Airport Aprons

Ferryman

Borg

Thirde

et al. 2005

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. This paper presents a complete visual surveillance system for automatic scene interpretation of airport aprons. The system comprises two main modules -Scene Tracking and Scene Understanding. The Scene Tracking module is responsible for detecting, tracking and classifying the semantic objects within the scene using computer vision. The Scene Understanding module performs high level interpretation of the observed objects by detecting video events using cognitive vision techniques based on spatio-temporal reasoning. The performance of the system is evaluated for a series of pre-defined video events specified using a video event ontology.

show abstract

Section: Resultsmentioning

confidence: 99%

“…The evaluation process is described in more detail in [1]. Of these algorithms, the colour mean and variance method was selected [13], after taking into account processing efficiency and sensitivity.…”

Section: Motion Detectionmentioning

confidence: 99%

Automated Scene Understanding for Airport Aprons

Ferryman

Borg

Thirde

et al. 2005

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…NRM is based on the pixelwise mismatches between the ground truth and the binarized image [26]. It combines the false negative rate NR FN and the false positive rate NR FP .…”

Section: Nrm (Negative Metric Rate)mentioning

confidence: 99%

“…The misclassification penalty metric MPM evaluates the binarization result against the ground truth on an object-by-object basis [26]:…”

Section: Mpm (Misclassification Penalty Metric)mentioning

confidence: 99%

Text Extraction from Historical Document Images by the Combination of Several Thresholding Techniques

Sari

Kefali

Bahi

2014

Advances in Multimedia

View full text Add to dashboard Cite

This paper presents a new technique for the binarization of historical document images characterized by deteriorations and damages making their automatic processing difficult at several levels. The proposed method is based on hybrid thresholding combining the advantages of global and local methods and on the mixture of several binarization techniques. Two stages have been included. In the first stage, global thresholding is applied on the entire image and two different thresholds are determined from which the most of image pixels are classified into foreground or background. In the second stage, the remaining pixels are assigned to foreground or background classes based on local analysis. In this stage, several local thresholding methods are combined and the final binary value of each remaining pixel is chosen as the most probable one. The proposed technique has been tested on a large collection of standard and synthetic documents and compared with well-known methods using standard measures and was shown to be more powerful.

show abstract

“…Therefore, the evaluation with these higher-level events directly targets the overall semantic interpretation of the scene. Evaluation methods on the pixel-level [1], frame-level [1,2] or object trajectory level [2,22] in contrary would only target the evaluation of sub-components of an algorithm.…”

Section: Event-based Performance Evaluationmentioning

confidence: 99%

Multi-object tracking evaluated on sparse events

Roth

Koller-Meier

Gool

2009

Multimed Tools Appl

View full text Add to dashboard Cite

This article presents a visual object tracking method and applies an eventbased performance evaluation metric for assessment. The proposed monocular object tracker is able to detect and track multiple object classes in non-controlled environments. The tracking framework uses Bayesian per-pixel classification to segment an image into foreground and background objects, based on observations of object appearances and motions in real-time. Furthermore, a performance evaluation method is presented and applied to different state-of-the-art trackers based on successful detections of semantically high level events. These events are extracted automatically from the different trackers an their varying types of low level tracking results. Then, a general new event metric is used to compare our tracking method with the other tracking methods against ground truth of multiple public datasets.

show abstract

Evaluation of Motion Segmentation Quality for Aircraft Activity Surveillance

Cited by 28 publications

References 11 publications

Automated Scene Understanding for Airport Aprons

Automated Scene Understanding for Airport Aprons

Text Extraction from Historical Document Images by the Combination of Several Thresholding Techniques

Multi-object tracking evaluated on sparse events

Contact Info

Product

Resources

About