Accelerometer Based Digital Video Stabilization for Security Surveillance Systems

Drahanský, Martin; Orság, Filip; Hanáček, Petr

doi:10.1007/978-3-642-10847-1_28

Cited by 5 publications

(11 citation statements)

References 2 publications

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“…Odelga et al [ 24 ] successfully stabilized a video with a low computation load by using the orientation data from the support base relative to a horizontal frame with no feature tracking applied in the design. Drahansky et al [ 25 ] used an accelerometer to estimate a local motion vector for calculating a smooth value in a global motion vector, while Karpenko et al [ 3 ] used only gyroscope data to create rolling shutter wrapping for video stabilization. Additionally, other researchers have integrated IMU data and feature tracking.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Motion Estimation for Video Stabilization Based on an IMU Sensor

Auysakul

Pooneeth

2018

Sensors

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Recorded video data must be clear for accuracy and faster analysis during post-processing, which often requires video stabilization systems to remove undesired motion. In this paper, we proposed a hybrid method to estimate the motion and to stabilize videos by the switching function. This method switched the estimated motion between a Kanade–Lucus–Tomasi (KLT) tracker and an IMU-aided motion estimator. It facilitated the best function to stabilize the video in real-time as those methods had numerous advantages in estimating the motion. To achieve this, we used a KLT tracker to correct the motion for low rotations and an IMU-aided motion estimator for high rotation, owing to the poor performance of the KLT tracker during larger movements. Furthermore, a Kalman filter was used to remove the undesired motion and hence smoothen the trajectory. To increase the frame rate, a multi-threaded approach was applied to execute the algorithm in the array. Irrespective of the situations exposed to the experimental results of the moving camera from five video sequences revealed that the proposed algorithm stabilized the video efficiently.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Motion Estimation for Video Stabilization Based on an IMU Sensor

Auysakul

Pooneeth

2018

Sensors

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“…From each list, the eight lowest values are taken and their coordinates become the local motion vectors. This is an improvement suggested by [1] in order to enable stabilization of frames without clear edges (e.g. desert, sea, snow).…”

Section: Local Motion Estimationmentioning

confidence: 99%

“…In a lot of these cases, high resolution, high frames count per second and steady image without parasitic effects like shake, jitter and blur is required. This is due to requirements for successful post-processing like target tracking or movement detection [1].…”

Section: Introductionmentioning

confidence: 99%

CUDA Accelerated Real-time Digital Image Stabilization in a Video Stream

Pacura¹,

Drahanský²

2016

IJSEIA

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“…In the context of photography, data captured by motion sensors is exploited for shake analysis in [27], wherein the authors target the detection of non-shaky temporal segments during which allowing the camera to capture a photo. In [8] the authors analyze sensor data in real-time for video stabilization in security surveillance systems. However they do not discuss the details of the actual analysis being performed.…”

Section: Prior Artmentioning

confidence: 99%

“…In this way, we mark those video segments which have been detected as shaky, for example in order to exclude them in a multi-camera video production or in the results of a query for the case of multimedia retrieval applications. Our method works in a different way than [8] and we will give more details in Section 4.2.1.…”

Section: Prior Artmentioning

confidence: 99%

Multimodal extraction of events and of information about the recording activity in user generated videos

Cricri

Dabov

Curcio

et al. 2012

Multimed Tools Appl

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In this work we propose methods that exploit context sensor data modalities for the task of detecting interesting events and extracting high-level contextual information about the recording activity in user generated videos. Indeed, most camera-enabled electronic devices contain various auxiliary sensors such as accelerometers, compasses, GPS receivers, etc. Data captured by these sensors during the media acquisition have already been used to limit camera degradations such as shake and also to provide some basic tagging information such as the location. However, exploiting the sensor-recordings modality for subsequent higher-level information extraction such as interesting events has been a subject of rather limited research, further constrained to specialized acquisition setups. In this work, we show how these sensor modalities allow inferring information (camera movements, content degradations) about each individual video recording. In addition, we consider a multi-camera scenario, where multiple user generated recordings of a common scene (e.g., music concerts) are available. For this kind of scenarios we jointly analyze these multiple video recordings and their associated sensor modalities in order to extract higher-level semantics of the recorded media: based on the orientation of cameras we identify the region of interest of the recorded scene, by exploiting correlation in the motion of different cameras we detect generic interesting events and estimate their relative position. Furthermore, by Multimed Tools Appl (2014) analyzing also the audio content captured by multiple users we detect more specific interesting events. We show that the proposed multimodal analysis methods perform well on various recordings obtained in real live music performances.

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Accelerometer Based Digital Video Stabilization for Security Surveillance Systems

Cited by 5 publications

References 2 publications

A Hybrid Motion Estimation for Video Stabilization Based on an IMU Sensor

A Hybrid Motion Estimation for Video Stabilization Based on an IMU Sensor

CUDA Accelerated Real-time Digital Image Stabilization in a Video Stream

Multimodal extraction of events and of information about the recording activity in user generated videos

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