Fast and robust video stabilisation with preserved intentional camera motion and smear removal for infrared video

IET Image ProcessingVolume 14, Issue 2 p. 376-383 Research ArticleFree Access Fast and robust video stabilisation with preserved intentional camera motion and smear removal for infrared video Sudhir Khare, Corresponding Author Sudhir Khare sudhir_khare@hotmail.com Instruments Research and Development Establishment, DRDO, Raipur Road, Dehradun, India Indian Institute of Technology, Roorkee, IndiaSearch for more papers by this authorManvendra Singh, Manvendra Singh Instruments Research and Development Establishment, DRDO, Raipur Road, Dehradun, India Indian Institute of Technology, Roorkee, IndiaSearch for more papers by this authorBrajesh Kumar Kaushik, Brajesh Kumar Kaushik orcid.org/0000-0002-6414-0032 Indian Institute of Technology, Roorkee, IndiaSearch for more papers by this author Sudhir Khare, Corresponding Author Sudhir Khare sudhir_khare@hotmail.com Instruments Research and Development Establishment, DRDO, Raipur Road, Dehradun, India Indian Institute of Technology, Roorkee, IndiaSearch for more papers by this authorManvendra Singh, Manvendra Singh Instruments Research and Development Establishment, DRDO, Raipur Road, Dehradun, India Indian Institute of Technology, Roorkee, IndiaSearch for more papers by this authorBrajesh Kumar Kaushik, Brajesh Kumar Kaushik orcid.org/0000-0002-6414-0032 Indian Institute of Technology, Roorkee, IndiaSearch for more papers by this author First published: 06 January 2020 https://doi.org/10.1049/iet-ipr.2019.0764Citations: 1AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Border military surveillance is one of the demanding and challenging tasks for any nation. Thermal (infrared) camera, which works on the infrared domain, provides complete visual sequences even in pitch dark night conditions. When the video is recorded from a thermal camera mounted on a vehicle, the output video is unstabilised with poor visual quality due to unintentional camera motion. These unwanted motions also introduce smear. Furthermore, there may be situations when a camera is moved intentionally to capture target. This study proposes a fast and robust algorithm for auto stabilisation of videos with smear removal while keeping the intentional motion of camera. This algorithm is developed under the framework of speeded up robust features matching. The proposed algorithm is capable of correcting both motions, i.e. translation as well as rotational. Quality improvement of up to 21 dB is achieved in the stabilised output videos. 1 Introduction Border surveillance is one of the most challenging tasks for any nation to protect its boundary against any intrusion. It is seen that most of the intruders try to cross the border during night. The thermal camera provides viewing under pitch dark night conditions. These thermal video cameras are usually mounted on border on a pole and transmit the videos wirelessly to the control room. These thermal cameras are also mounted on patrolling military vehicles and video is recorded by holding the camera in hand or mounted on vehicle platform. Making a stable video using these devices is a challenging task especially when a hand-held camera or vehicle mounted camera is used. Therefore, there is a requirement to stabilise the thermal video before viewing with enhanced visual quality. The human visual sensing system is pleasant and appreciates smooth motions but unpleasant when there are high-frequency motions, known as jitter. Unintentional hand shake or vibrating platforms causes disturbances in the video with decreased visual quality. This undesired jitter can be removed by estimating global camera motion (frame motion). This study proposes a fast and robust digital video stabilisation algorithm for thermal videos using visual speeded up robust features (SURF). SURF is measured in first immediate frame, i.e. reference frame (RF) and next consecutive frame is candidate frame (CF). Then strongest features are matched between these two frames. All inlier feature points correspond to background scene. The vector difference of these inlier features gives the global motion vector (GMV), i.e. the displacement of CF w.r.t. RF. Now, the CF is shifted in the opposite direction but with the same magnitude of GMV for getting stabilised CF. Now, this CF is renamed as RF, the procedure is repeated for the next CF, hence only two frames are processed at a time for fast online stabilisation. The affine parameters obtained from transformation matrix (TM) are used to get the point spread function (PSF) to deconvolve the current stabilised CF to remove the smear. For retaining the intentional motion, the first few frames (we took 2 s video, i.e. 50 frames) GMVs are stored and low-pass filtering is applied to remove high-frequency jitters and retain low-pass intentional vectors. Then these low-passed GMV values are used to stabilise the CF for respective filtered GMV value. This algorithm resulted in stabilised, high-quality sharp video w.r.t. input unstabilised blurred video. In our experiments, we could improve the video up to 21 dB with retained intentional motion. There are several algorithms available for video stabilisation, however, to the best of our knowledge, there is not a single algorithm dedicated for infrared (IR) videos for stabilisation with intentional motion and smear removal. There are three basic techniques available for video stabilisation–optical image stabilisation (OIS), electronics image stabilisation (EIS), and digital image stabilisation (DIS). In the OIS method, an optical lens assembly compensates the disturbance in the opposite direction. This technique is usually for very low amplitude vibrations such as atmospheric turbulences. In the EIS method, the camera is itself packaged in a mechanical housing equipped with rate of disturbance sensor (gyro), motor assembly, and servo electronics. The disturbance rate sensed by gyro is processed in servo electronics having a typical proportional integral derivative controller. The motor keeps on correcting gyro feedback to smallest specified error around zero value by moving camera in opposite direction thus, stabilisation is achieved. This method requires expensive mechanical gimbal, sensors, actuators, servo electronics, and also depends on camera physical dimensions. In the third type of stabilisation method, i.e. DIS is purely software based and independent of camera physical dimensions. Using image processing techniques, motion of each frame is estimated and compared with consecutive frame to get the GMV as shown in Fig. 1. There are three major steps in digital video stabilisation, i.e. motion estimation, motion compensation, and frame synthesis. After getting GMV, CF is stabilised and sent to frame synthesiser block for producing online stabilised videos. The block diagram for video stabilisation is shown in Fig. 2. Fig. 1Open in figure viewerPowerPoint Forward and backward global motion estimation Fig. 2Open in figure viewerPowerPoint Digital video stabilisation GMV can be estimated by either using a feature- or pixel-based approach. The feature-based approach is faster and more effective than pixel based, as it is illumination independent. 2 Related work Many techniques and algorithms have been developed and applied to the unstable video to improve its virtue. One such related work is given in [1] which uses an inter-frame homographic estimation motion model, which efficiently stabilises the video. This motion model updates at each frame and prevents errors occurring due to parallax or scene change. Many algorithms have used the feature trajectory method under Kalman or Median filter framework for video stabilisation. Information is lost in each frame while using this method. A method to reduce information loss is given in [2]. This is based on affine scale invariant feature transform (SIFT) feature matching to estimate the affine matrix and then using Gaussian filter. Real-time video stabilisation techniques are given in [1, 3, 4]. Field programmable gate array is used for stabilisation purpose called embedded real-time video stabilisation system by adopting an integral projection matching approach [5]. The global motion is filtered from high-frequency components. It can be used for PAL videos where the temporal gap in-between two frames is in the order of 40 ms. Kalman filter-based motion smoothing is proposed with video stabilisation using the mosaicing algorithm in [1] without losing any information. It separates the GMV and local motion vectors and stabilisation is carried out by compensating the GMV in opposite direction. The work of stabilisation and tracking of an egocentric video captured by body worn cameras is given in [6]. It is extremely difficult to analyse an egocentric video. It categorises the history of stabilisation and tracking according to the number of objects in the video, camera motion vector, the type, the size, the relative motion between objects. This algorithm estimates the camera path and tracks multiple objects in the frame. Different stabilisation approaches are proposed in [7-9]. A novel fast image stabilisation based on block matching is given in [7, 10], where local similarity and matching effectiveness is taken into account. Shaky motions are estimated through long and short feature trajectories [8]. Video stabilisation is converted into a quadratic energy minimisation problem for image composition, which can be solved using a solver for sparse linear systems. Discontinuous depth variation is the main challenge for near range videos. By compensating rotational motions and ignoring translational motion [9] the near range videos can be stabilised. The hybrid of 2D and 3D video stabilisation methods is used in this study. Another method in which features of each frame are detected by the features from accelerated segment test method and then feature matching is done using rotated binary robust independent elementary features called as ORB [11]. This is efficient in feature detection and matching without the estimation of GMVs independently. The advantage of both real-time and offline videos is taken into consideration in [12], where input frames are taken offline and stabilised by warping the key-frame, which increases visual stability. For keeping intentional motion undisturbed, the next key-frame update is done by measuring the inconsistency between present key-frame and the key-frames that are incoming. This algorithm works well with fast disturbances also. Stabilisation of videos becomes more challenging when camera is mounted on a running vehicle. To stabilise these videos, a novel approach is proposed in [13], where authors take feedback of trajectories from previous frames. These trajectories are divided into foreground and background. Then background trajectory is used to stabilise the video. Adaptive GMV-based video stabilisation is given in [14-16]. Intentional motion is preserved in the algorithm proposed in [17], where authors keep the intentional motion while rejecting shaky global motion. A comprehensive image performance analysis criterion is provided in [18-23]. These methods present the guidelines for assessing visual quality. The use of SIFT in the stabilisation of thermal IR videos captured by the unmanned aerial vehicles is explained in [24]. Motion estimation in IR videos is a difficult task, a research paper is presented in [25], where the motion estimation is based on determination of appropriate blocks with maximum information and then the videos are stabilised. This work is extended for real-time video stabilisation of infrared videos in [26]. 3 Video stabilisation algorithm Thermal images are difficult to process because these images are of low quality, poor contrast and brightness, less number of features and noisy. Special techniques are used to handle thermal videos. We pre-process the frames to convert them in grey levels only. SURF-based feature matching is carried out due to the multi-scale nature and fast computational capability. We assume that the first frame is stable and taking this frame as a reference, we stabilise the next frame of video. To find out the shift, first, we match the features of the first and second frames. Let the second frame be shifted from the first frame by a vector P(x, y), where shift in the horizontal direction is ‘x’ and in the vertical direction is ‘y’. This vector P(x, y) is called the global translation motion as shown in Fig. 3. The next frame will be stabilised by the same translational motion vector, but the direction of shift is opposite to the calculated GMV. We estimate the angle of rotation as global rotation motion as depicted in Fig. 4, and stabilise the frame in angular direction also. A video stabilisation process flow diagram is depicted in Fig. 5. Fig. 3Open in figure viewerPowerPoint Global translational motion Fig. 4Open in figure viewerPowerPoint Global rotation motion Fig. 5Open in figure viewerPowerPoint Processing for video stabilisation using SURF SIFT also matches features in the two frames using first- and second-order differential operators [5]. We use the SURF technique here instead of SIFT because SURF is a speeded-up technique that is computationally fast. It is robust and detects the points of interest features. It has a similar descriptor as in SIFT and extracts the similar gradient information which describes the intensity content of the features with respect to the surrounding of the interest point. The gain in speed of the SURF method is because of integral image processing. This integral image processing reduces the computation as we use simple box filters instead of calculating Gaussian method-based kernels. We take only 200 strongest features for processing, which are sufficient. The detected features, features matching including both inliers and outliers and feature matching with only inliers for four different experimental videos are shown in Figs. 6-9. Nearest neighbour distance ratio matching criterion is used to extract most significant correspondences of inlier feature points (frame motion) using a similarity distance dk,m and threshold λ. If the query descriptor is denoted as Xk, its most similar candidate as Ym1 and second closest Ym2 then computation of Ym1 and Ym2 are carried out using (1) and (2) (1) (2)comparing the corresponding ratio of distances with a fixed threshold value (λ = 0.8), so that (3)All feature points satisfying this condition are considered as inlier points else outliers. From all the matched points we consider only inliers for the estimation of the GMV between the two frames and reject the outliers as shown in Figs. 6d, 7d, 8d, and 9d. All outliers correspond to moving objects and represent local motion vector. While all inlier points will correspond to background objects, which are stationary and represents GMV. Affine transformation is applied in inlier points for angular rotation of frame image. The input video is read and first and second frame images are stored. Now features of the second frame are extracted and matched using SURF. To perform recovery of distortions, we need a combination of scaling, translation, shearing, and rotation, which is applied by mapping variables from the input image to distorted image. A corrected output image is received. Since the unstabilised image may contain translation and rotation both, affine transformation is represented by (4)where (x1,y1) is the position of pixels in RF and (x2,y2) in the distorted frame. Rot. is the rotational matrix given by (5)Here is the angle by which the distorted image is rotated. Trans. is the translation matrix given by (6)where and are the distortions in x and y directions, respectively. For the next loop, the corrected frame is taken as RF and next frame as a deformed frame. TM is given by (7)When a video has both intentional as well as unintentional motions, then intentional motion needs to be preserved. It is noted that intentional motion will have very low-frequency usually 0.1–1.0 Hz only with low magnitude. On the other hand, the unintentional motion will have high vibration frequency. Therefore, the proposed algorithm intelligently keeps this low-frequency GMV while rejecting high-frequency GMVs. Once the feature matching is completed, CF has to be transformed in such a way that all inlier background feature points are moved back to (−xg, −yg) and −θ position (as they were positioned in RF). For this standard, affine transformation is used because we know the values of xg, yg, and θ parameters. After extracting the key points in each and every frame of the video sequence, their frame-to-frame correspondence is established on the basis of the Euclidean distance between the SURF feature vectors for each pair of points in two consecutive frames, i.e. point Xi = (xi, yi) in the kth frame corresponds to point X′j = (xj, yj) in the (k + 1)th frame if and only if the feature vectors for these two points are mutually closest to each other in Euclidean distance sense. After finding the matches between the key points, it is now necessary to estimate the global motion. For this, assuming that the camera motion includes only translational and rotational motions, the affine transform matrix as shown in (8) transforms the set of points in the kth frame to the corresponding set of points in the (k + 1)th frame is estimated (8)GMVs are estimated by processing two frames at a time, i.e. RF and CF. Once the GMV is known, it is stored in an array and the CFs are compensated with an exact magnitude but in the opposite direction to get a stabilised frame. Then the present stabilised CF is renamed as RF and next consecutive frame is taken as CF. Now the GMV between these two frames is estimated. We continue this process to estimate the GMVs and stabilises the video in real time. Fig. 6Open in figure viewerPowerPoint Feature detection and matching in thermal video 1 (a) Detected features in RF, (b) Detected features in the distorted frame, (c) Feature matching (including outliers), (d) Feature matching (only inliers) Fig. 7Open in figure viewerPowerPoint Feature detection and matching in thermal video 2 (a) Detected features in RF, (b)Detected features in distorted frame, (c)Feature matching (including outliers), (d)Feature matching (only inliers) Fig. 8Open in figure viewerPowerPoint Feature detection and matching in thermal video 3 (a) Detected features in RF, (b)Detected features in distorted frame, (c)Feature matching (including outliers), (d)Feature matching (only inliers) Fig. 9Open in figure viewerPowerPoint Feature detection and matching in thermal video 4 (a) Detected features in RF, (b)Detected features in distorted frame, (c)Feature matching (including outliers), (d)Feature matching (only inliers) However, for first few seconds (we took initial two seconds, i.e. ∼50 frames) only GMVs are calculated and stored in an array. During this 2 s, we did not stabilise the video. For the first 50 frames, GMV array will have 49 entries. Now for the 51st frame, GMV is calculated and stored in the GMV array as the 50th entry. Now, this GMV array is low pass filtered to remove high-frequency jitter/vibration values. The value obtained corresponding to the 51st frame is the desired intentional GMV. Now this CF is stabilised. When the next frame appears, i.e. frame number 52nd, we process only last 50 values in the GMV array, i.e. first entry is discarded to stabilise the 52nd frame. Then, this process continuous until the expiry of frames. The resultant video is stabilised while keeping only intentional motion and removing all high-frequency components. We have chosen a Chebyshev type I, second-order filter as given in (9) for getting the desired intentional GMVs (9)where is the ripple factor, is the cut-off frequency, Rn is the Chebyshev polynomial of the nth order. It can be seen from Figs. 10a and b that green colour intentional motion is preserved, while red colour unintentional motion is rejected. Fig. 10Open in figure viewerPowerPoint Segregated intentional and unintentional GMV Finally, the stabilised frames are sent to frame synthesiser block for the production of stabilised video online. Since only two frames are considered for processing, this method stabilises the video online in real time. The proposed methodology is robust as it considers SURF. The algorithm also corrects the motion smear introduced through unintentional vibration motion. It deblurs the video to give sharp and stable output as shown in Figs. 11a and b. After stabilising the video, the blur is removed using the affine TM elements as given in (9). The affine TM gives the values of ) and . The value of is calculated by (10)where ‘’ is the angle of blur between the consecutive frames. A PSF is the response of an imaging system to a point object. Let, D be the degraded/blurred image, Q be the original image, then PSF can be given as The PSF g(x, y) of a camera is defined as the response of the camera to a point (single pixel) object. Consequently, motion smear may be removed from an input blurred image I′(x, y) by deconvolving it with the PSF. PSF corresponding to linear motion smear is given by (11) and (12) (11)where (12)smear length L and smear angle , used in modelling g(x, y) can be estimated from the previously obtained frame-to-frame global motion parameters; L corresponds to the translational motion and corresponds to the rotation as (13)Once an estimate of g(x, y) is obtained, Lucy–Richardson algorithm is applied which is an iterative deconvolution method for getting sharp images for smear removal. The deconvolution equation is given as (14)where ‘x’ is the distance between ‘i’ and ‘j’. The input unstabilised blurred image and output stabilised sharp images are shown in Fig. 11 for four different experimental videos. Fig. 11Open in figure viewerPowerPoint Image stabilisation with smear removal results (a) Unstabilised and blurred frames, (b) Stabilised and sharp frames The flowchart of the proposed algorithm is given in Fig. 12. Fig. 12Open in figure viewerPowerPoint Flowchart for the video stabilisation 4 Experimental results Some sample stabilised frames are shown in Fig. 13 for four videos. Fig. 13Open in figure viewerPowerPoint Stabilised frames for four videos (a) Video 1, (b) Video 2, (c) Video 3, (d) Video 4 To validate the accuracy and performance of the proposed algorithm, performance analysis is carried out [27]. Four videos were taken for performance analysis. 4.1 Ground truth generation In order to perform quantitative analysis, ground truth videos are generated. For our experiment, we took four videos having 50 frames each as shown in Table 1. To generate ground truth video, we generated two different signed integer arrays for xg and yg coordinates, respectively. Now each frame of a particular video was shifted with (xg, yg) resulting in unstabilised video with known GMV. Now with our proposed algorithm, we estimated this frame motion in the unstabilised video (x′g, y′g). The error in the estimation provides the accuracy of the proposed algorithm. Since these randomly generated values may cause much flicker in the stable video, therefore, this array is post-processed through a five-tap moving average filter to make this data smoother resulting in smooth unstabilised videos. The five-tap moving average filter is a low pass filter that takes a running average of five consecutive array values. Five-tap filter is given by (15) and (16). The structure of filter design is shown in Fig. 14(15) (16)where (λH, λV) is the ground truth generated GMV. Table 1. ITF of unstabilised and stabilised videos Thermal video RMSE ITF unstab. ITF stabilised ITF gain video 1 0.2287 23.3541 43.4349 20.0807 video 2 0.0815 21.2067 42.1661 20.9594 video 3 0.0802 17.4236 25.7563 8.3326 video 4 11.8454 27.9776 33.7391 5.7615 Fig. 14Open in figure viewerPowerPoint Five-tap moving average filter 4.2 Error analysis Each frame in the ground truth video is unstabilised using smoothened random values or ground truth GMV (λH, λV). Then the unstabilised video is stabilised using the proposed algorithm after estimating the GMVs (Xact, Yact) through SURF. The measured GMV is compared with ground truth GMV and error is calculated. It is found that the error in both x and y directions does not exceed a value of 0.5 pixels where (λH,i, λV,i) is the GMV coordinate of the ground truth video and (Xact,i, Yact,i) is the actual GMV coordinate of the stabilised video's the ith frame. The ±0.5 pixel error is recorded due to the integer level processing and rounding fractional sub-pixel operations. Thus, this algorithm gives us ±0.5 pixel accuracy. The GMV graphs are shown in Figs. 15-18. Fig. 15Open in figure viewerPowerPoint Horizontal and vertical error in video 1 Fig. 16Open in figure viewerPowerPoint Horizontal and vertical error in video 2 Fig. 17Open in figure viewerPowerPoint Horizontal and vertical error in video 3 Fig. 18Open in figure viewerPowerPoint Horizontal and vertical error in video 4 4.3 Root mean square error analysis (RMSE) RMSE is analysed between the two GMVs, i.e. (λH,i, λV,i) and (Xact,i, Yact,i). RMSE is given by (17)where ‘N’ is the number of frames. RMSE value should be very low for good stabilisation. 4.4 Mean square error (MSE) analysis MSE is calculated for two consecutive image frames. MSE is calculated by using the following equation: (18)Here the image has a total number of rows ‘m’ and total number of column ‘n’. The two consecutive frames are ‘I’ and ‘K’ and its index is represented by ‘i’ and ‘j’. MSE for unstabilised and stabilised video is taken. Very low values of MSE observed. 4.5 Peak signal-to-noise ratio (PSNR) An unstabilised video is of poor quality. We measure the quality of a video in terms of PSNR in between two successive frames. A stabilised video will have a higher PSNR value because MSE of stable video will be less, in contrast to this, the PSNR value of unstabilised video will be low as the MSE of unstabilised video will be more. PSNR is given by (19)where Imax represents the maximum intensity value of any pixel in the frame that is being analysed. This value is 255 for grey scale images. Higher the value of PSNR higher will be the quality of stabilised video. 4.6 Inter-frame transformation fidelity (ITF) ITF is the measure of the temporal smoothness of an image. It defines the overall quality of a video. An unstable video will have poor quality and shaky. On the other hand, the stabilised video will be smooth and of pleasing visual quality. The ITF for video before stabilisation and after stabilisation are compared in terms of dB as given in (20) (20) 4.7 Stabilisation accuracy On computing, an ITF gain of 6 to 21 dB is achieved as shown in Table 1. All experiments were carried out on Windows 10 Home Single Language 64-bit Version: 17763.503 with 4 GB RAM, Intel(R) Core(TM) i3-6006U CPU at 2.00 GHz configuration. The MATLAB R2014a version was used. 5 Conclusion This study proposes an algorithm for stabilising thermal video using the SURF matching technique. The algorithm is robust and is capable of correcting any linear and rotational motion of the frame. The algorithm removes the motion smear and also preserves the intentional motion assisted by user. High-quality smooth infra-red videos are obtained using the proposed method. Experimentally it is verified that around 5–21 dB ITF gain improvement is achieved. 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