Video image quality analysis for enhancing tracker performance

Irvine, John; Wood, Richard J.; Reed, David A.; Lepanto, Janet A.

doi:10.1109/aipr.2013.6749326

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…Given various applications, there was a need to assess the image [57,58,59,60]. Recent efforts included VNIIRS in support of event recognition [61,62] and tracker performance [63,64]. VNIIRS supports all levels of information fusion.…”

Section: Vniirsmentioning

confidence: 99%

Application of VNIIRS for target tracking

Blasch¹,

Kahler²

2015

SPIE Proceedings

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The Motion Imagery Standards Board (MISB) has created the Video National Imagery Interpretability Rating Scale (V-NIIRS). VNIIRS extends NIIRS to scene characterization from streaming video to include object recognition of various targets for a given size. To apply VNIIRs for target tracking, there is a need to understand the operating conditions of the sensor type, environmental phenomenon, and target behavior (SET). In this paper, we explore VNIIRS for target tracking given the sensor resolution to support the relative tracking performance using track success. The relative assessment can be used in relation to the absolute target size associated with the VNIIRS. In a notional analysis, we determine the issues and capabilities of using VNIIRS video quality ratings to determine track success. The outcome of the trade study is an experiment to understand how to use VNIIRS can support target tracking evaluation.

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Section: Vniirsmentioning

confidence: 99%

Application of VNIIRS for target tracking

Blasch¹,

Kahler²

2015

SPIE Proceedings

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“…(Figure 8) Further, suitable metrics, computed in real time, are accurate predictors of tracking challenges. 1,4,6 We computed a set of image metrics that previous research suggested would be indicative of tracker performance, including measures of jitter and temporal instability, texture-based measures of sensor noise, local measures of variance that capture the clutter complexity, and edge metrics to measure scene sharpness. The dependent variable in this model is θ, which the estimated separability parameter derived from generalized FROC analysis of the trackers.…”

Section: Image Qualitymentioning

confidence: 99%

“…(Table 4) Our model shows good performance across a range of image quality, with R2 equal to 0.778. Additional experiments, using a larger set of video clips, are validating the model 6. …”

mentioning

confidence: 99%

Enhancing event detection in video using robust background and quality modeling

et al. 2014

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Automated event recognition in video data has numerous practical applications for security and transportation. The ability to recognize events in practice depends on precisely detecting and tracking objects of interest in the video data. Numerous factors, such as lighting, weather, camera placement, scene complexity, and data compression can degrade the performance of automated algorithms. As a preprocessing step, developing a set of robust background models can substantially improve system performance. Our object detection and tracking algorithms estimate the object position and attributes within the context of this model to provide more reliable event recognition under challenging conditions. We present an approach to robustly modeling the background as a function of the data acquisition conditions. One element of this approach is automated assessment of the image quality which informs the choice of which background model to use for a given video stream. The video quality model rests on a suite of image metrics computed in real-time from the video, whereas the background models are constructed from historical data collected over a range of conditions. We will describe the formulation of both models. Results from a recent experiment will quantify the empirical performance for recognition of events of interest.

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“…These approaches are usually based on natural scene statistics (NSS) or deep relevant quality features that account for post-capture distortions such as blur, additive noise, and uneven illumination. In addition, most existing literature on the effect of distortions on VOT performance [22]- [24] deals with post-capture distortions, such as blurring caused by shaking motion [25], and deblurring [26].…”

Section: Introductionmentioning

confidence: 99%

Quality Aware Features for Performance Prediction and Time Reduction in Video Object Tracking

et al. 2022

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The existing body of work on video object tracking (VOT) algorithms has studied various image conditions such as occlusion, clutter, and object shape, which influence video quality and affect tracking performance. Nonetheless, there is no clear distinction between the performance reduction caused by scene-dependent challenges such as occlusion and clutter, and the effect of authentic in-capture and postcapture distortions. Despite the plethora of VOT methods in the literature, there is a lack of detailed studies analyzing the performance of videos with authentic in-capture and post-capture distortions. We introduced a new dataset of authentically distorted videos (AD-SVD) to address this issue. This dataset contains 4476 videos with different authentic distortions and surveillance activities. Furthermore, it provides benchmarking results for evaluating ten state-of-the-art visual object trackers (from VOT 2017-2018 challenges) based on the proposed dataset. In addition, this study develops an approach for performance prediction and qualityaware feature selection for single-object tracking in authentically distorted surveillance videos. The method predicts the performance of a VOT algorithm with high accuracy. Then, the probability of obtaining the reference output is maximized without executing the tracking algorithms. We also propose a framework to reduce video tracker computation resources (time and video storage space). We achieve this by balancing processing time and tracking accuracy by predicting the performance in a range of spatial resolutions. This approach can reduce the execution time by up to 34% with a slight decrease in performance of 3%.INDEX TERMS Video Object Tracking, in-capture and post-capture distortions, video quality assessment, video tracking prediction.

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Video image quality analysis for enhancing tracker performance

Cited by 8 publications

References 16 publications

Application of VNIIRS for target tracking

Application of VNIIRS for target tracking

Enhancing event detection in video using robust background and quality modeling

Quality Aware Features for Performance Prediction and Time Reduction in Video Object Tracking

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