Automatic aircraft extraction using video matting and frame registration

IET Image ProcessingVolume 14, Issue 8 p. 1628-1635 Research ArticleFree Access Automatic aircraft extraction using video matting and frame registration Benish Amin, Corresponding Author Benish Amin benishamin@students.mcs.edu.pk National University of Sciences and Technology (NUST), Islamabad, PakistanSearch for more papers by this authorMuhammad Mohsin Riaz, Muhammad Mohsin Riaz Center for Advanced Studies in Telecommunication (CAST), COMSATSn, Islamabad, PakistanSearch for more papers by this authorAbdul Ghafoor, Abdul Ghafoor National University of Sciences and Technology (NUST), Islamabad, PakistanSearch for more papers by this author Benish Amin, Corresponding Author Benish Amin benishamin@students.mcs.edu.pk National University of Sciences and Technology (NUST), Islamabad, PakistanSearch for more papers by this authorMuhammad Mohsin Riaz, Muhammad Mohsin Riaz Center for Advanced Studies in Telecommunication (CAST), COMSATSn, Islamabad, PakistanSearch for more papers by this authorAbdul Ghafoor, Abdul Ghafoor National University of Sciences and Technology (NUST), Islamabad, PakistanSearch for more papers by this author First published: 15 May 2020 https://doi.org/10.1049/iet-ipr.2019.0067Citations: 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 This study presents a technique to automatically extract the aircraft from video sequences. The technique incorporates object tracking algorithm to separate the region of interest (ROI), i.e. movement of aircraft, from video sequence, to reduce the computational complexity. Trimaps are generated automatically for given ROI using non-rigid image registration to obtain the prior information of the object and alpha mattes are estimated for each frame using KNN matting algorithm. However, the alpha matte of static region is estimated only once, that is combined with the alpha matte of moving region to generate final alpha matte. Simulations performed on different videos show that the proposed technique not only reduces the computational complexity, but also gives competitive results by generating high quality alpha maps. 1 Introduction The image processing and machine vision is a technique of electronics field which is useful for many applications. Nowadays, image processing and machine vision approaches are successfully applied for classification, recognition or clustering of large data and have numerous applications in everyday life such as, self driving cars [1], speech recognition and classification [2], pedestrian detection [3], real-time traffic monitoring and safety [4], lung nodule detection [5] and many more. Object detection, object recognition and object tracking are nowadays three important processes in image and videos which have a variety of applications. Among them, extraction of aircraft from videos plays an important role in remote sensing interpretation especially for aircraft recognition/ classification. Aircraft recognition is useful in many applications, such as oil tank detection and oil spill detection, etc. In order to have exact aircraft recognition, accurate image segmentation should be done first. Image segmentation is an important technique widely used in many application fields, such as synthetic aperture radar (SAR) image registration [6], real-time pedestrian detection [3], scour depth prediction [7], iris tissue recognition [8], reservoir rock fracture recognition [9], change detection in SAR images [10], automatic detection and segmentation of blood vessels and pulmonary nodules [11], coastline detection [12] and ship classification [13] etc. In recent years, many techniques and algorithms have been developed for segmentation of aircrafts. The techniques incorporate saliency information in gradient [14] and frequency domain [15], intensity and edge information [16], texture and shape features using wavelet and fast discrete curvalet transform [17]. Another technique utilises anisotropic heat diffusion model [18] for target extraction. Recently, usage of convolutional neural networks (CNNs) for aircraft recognition has been increased. Many algorithms have been proposed in recent years for aircraft recognition based on CNN. A fully convolutional network that incorporates online exemplar mining has been proposed [19] to detect and segment aircrafts from aerial images. A fast-RCNN [20] that consists of sparse convolutional features to recognise aircrafts in complex scenarios is proposed. After that, another algorithm of CNN with saliency information is presented for aircraft detection [21], that has higher accuracy than fast-RCNN with very less number of negative samples. Recently, an algorithm based on constant false alarm rate is proposed for target detection and multilayer perception based CNN is applied for target classification [22]. Deep belief networks (DBNs) are becoming very considerable research field in machine vision, that have various applications [23]. The remarkable growth of deep learning techniques have enabled the researchers to apply DBNs for efficient aircraft recognition. In literature, a DBN was trained by utilising multiple gradient and threshold images and then fine-tuned by back-propagation to detect aircrafts from complex backgrounds [24]. Nowadays, SAR and inverse SAR techniques are widely used for automatic aircraft recognition and identification. In these techniques, the main problem is the speckle noise that is introduced during acquisition and transmission, and severely damages the image because of its multiplicative property. Thus, de-noising is an important pre-processing step for such applications. For that, deep neural network (DNN) methods have become more popular and reliable for image de-noising [25]. Recently, auto-encoder (AE) and stacked AE tools are adopted for reliable de-noising, and aircrafts are identified by applying CNN [26]. The technique is found to be efficient and reliable for aircraft identification and de-noising. Apart from DNN, texture recognition methods play important role for effective recognition and detection of aircrafts. The main challenge is the feature extraction from these images, that is difficult due to presence of speckle noise in SAR images. Akbarizadeh [27] proposed an efficient approach for texture recognition by extracting the texture feature using wavelet transform and generates a feature vector that consists of kurtosis value of wavelet energy feature of SAR image. After that, texture segmentation is performed using feature vector and level set function and support vector machine is trained to classify different textures. Another problem in extracting aircrafts from SAR images is the inhomogeneity of pixel intensities and discontinuities caused by scattering mechanism on aircraft surfaces. To address this issue, an active shape model based on ISOMAP is proposed to extract aircrafts from SAR images [28]. ISOMAP model extracts the shape information and provides flexibility to extract different kinds of aircrafts. To improve the recognition and classification accuracy of aircrafts from video sequences, it is important to extract/ segment the aircrafts from background effectively. The varying speed, shape, scale and rotations of aircraft made this process quite difficult and challenging. This study aims to present an accurate and efficient video matting approach that automatically extracts the aircraft from video frames. The main contributions of the study are summarised below: (i) Computational complexity of the technique is reduced by separating region of interest (ROI), i.e. moving aircraft region, using L1 tracker. (ii) Video matting requires a trimap, that identifies the foreground, background and unknown regions of a video frame. The user initialises the process by identifying the foreground (i.e. aircraft) for the first frame and the trimaps for the remaining frames are computed automatically using image registration (as it is inefficient and time consuming to generate trimaps manually for each frame.) (iii) KNN matting is performed to extract the aircraft from moving region, while the alpha matte of static region is estimated only once. In the end, both regions are combined to generate the final alpha matte. 2 Background study 2.1 Object tracking Object tracking refers to the task of labelling the tracked object by inserting a bounding box on each frame in video sequence. Many tracking algorithms have been proposed over last few years, with the aim to achieve precise tracking results. The methods include traditional feature extraction based tracking [29-33] as well as deep learning based tracking [34-36]. However, there are certain factors involve in terms of aircraft tracking, that may limit the accuracy of tracking algorithms, i.e. variable speed, shape changes, deformations, complex background, occlusions etc. This study utilises L1 tracking algorithm [29] to track the movement of aircraft and to separate the moving and static part of the video frame. L1 tracker performs real time object tracking by means of sparse sampling and accelerated proximal gradient approach. The technique provides better robustness and high performance for complex scenarios. 2.2 Image registration Image registration is the process of aligning two or more images of same scene (captured at different times or from different viewpoints) with respect to a reference image. The captured images are geometrically transformed into the coordinate system of reference image. Image registration is broadly classified into two categories: rigid and non-rigid registration. In rigid registration, simple transformations such as rotations, scale and translations, are applied to align the images w.r.t the reference image [37, 38]. However, non-rigid registration involves non-uniform transformations and makes significant deformations to transform images w.r.t reference image [39-41]. Image registration has wide applicability in medical and computer vision tasks. In this study, we have utilised the concept of non-rigid registration [39] to estimate the trimaps of video frames. Since aircraft tends to change the position, size and shape in video sequence, therefore several deformations occur while transforming the trimap of previous frame to the next frame. The technique handles free-form deformations efficiently and effectively. 2.3 Alpha matting Alpha matting is the technique to separate the foreground object from image, that is used in various applications, e.g. to extract objects accurately from images and videos [42, 43], to segment the defoused region [44] and to fuse multi-focus images [45] etc. The purpose of matting is to extract the foreground by finding the opacity value of unknown pixels that lie near the boundary of the object and are mixed between foreground and background. Matting techniques generally comprise of four categorise: sampling based methods [46, 47] (that collect nearby foreground and background colour samples to find opacity value of unknown pixels), propagation based methods [48, 49] (that define affinities between neighbouring pixels to find alpha value of unknown pixels), hybrid methods [50, 51] (combination of sampling and propagation based methods for optimisation) and machine learning based methods [52, 53] (that build models by learning rather than depending on model assumptions). This study adopts KNN matting technique [48] that is based on non-local principle and utilises closed-form solution [54] to find affinities between pixels. KNN matting produces good quality alpha mattes for images having high texture and complex background. Moreover, the technique provides robustness, thus produces accurate alpha mattes in less amount of time. Considering these advantages, KNN matting algorithm is used in proposed technique to extract the aircraft from video frames. 3 Proposed methodology The block diagram of the proposed technique is shown in Fig. 1. Let be the intensity of colour input video at location and and at time t, where represents red, green and blue colour bands, i.e. . To initialise the matting process, the alpha matte of first frame is computed by giving the first frame and its user generated trimap as input. KNN matting algorithm [48] is applied to generate alpha mattes of video frames, in such a way that pixels having similar appearance are expected to have similar alpha value. Considering this, the feature vector is defined (where h,s and v represents HSV coordinates). The similarity between two pixels is calculated by defining a kernel function, (1)where C be the least upper bound of . Fig. 1Open in figure viewerPowerPoint Block diagram of the proposed technique To reduce the computational complexity, moving region is tracked and separated from the video sequence. The proposed approach uses tracker [29] to track the movement of aircraft which operates in real time and have impressive accuracy with robust performance. Let be the center of window containing target, the location of at frame is, (2)where represents the tracker, represents target templates, trivial templates and norm parameters respectively. is the number of iterations in accelerated proximal gradient (APG) [55] approach, that is used to solve the minimisation model of tracker. represents the number of samples for sparse representation of target and trivial templates. is the deviation of samples in particle filter framework [56, 57], that provides an estimation of the target in the frame. are the number of particles and s represents the size of the template. The tracked region is separated from each frame in video sequence to obtain sub-frames . Video matting from now onwards will only be performed in sub-frames which will be added back and reframed with the alpha matte of previous frame to obtain the final alpha matte of current frame. Manual generation of trimaps for each video frame requires a lot of user intervention and is time consuming. In proposed technique, user is required to provide trimap for first frame only and the trimaps of next frames are estimated automatically using image registration. For that, each sub-frame is first converted into gray scale, (3) Consider two consecutive frames and corresponding to previous and current frames. Non-rigid registration [39] is performed. Since aircrafts change their location as well as shape when moving, therefore non-rigid registration is suitable to cater deformations. Non-rigid registration provides the control points that define the degree of non rigid deformation. This will help to transform the trimap of previous frame to estimate the trimap of next frame . The transformation is computed as [58], (4)where , , and . and are basis functions that are defined as Trimap divides the image into three regions, i.e. definite foreground region labelled as 1, definite background regions labelled as 0 and unknown region labelled as 0.5. Thus, the estimated trimap is passed through a multilevel Otsu thresholding algorithm to obtain two threshold values, i.e. (5)where represents multilevel thresholding operation. The threshold values obtained using multi-level Otsu thresholding algorithm divides the trimap into three regions as, (6) Each sub-frame along with respective trimap is passed to the matting process [48] to estimate the alpha matte of corresponding sub-frame. The final alpha matte is generated by adding the alpha matte of moving region to the alpha matte of previous frame . The final alpha matte is, (7) Fig. 2 shows the matting results obtained using automatic estimated trimaps. Fig. 2a shows the input frames to be processed, whereas Fig. 2b shows the tracking results obtained by applying Tracker [29]. The tracked object, i.e. the aircraft, is separated from the frames and trimaps are generated automatically as shown in Fig. 2c. The accuracy of estimated trimaps can be seen in Fig. 2d. The final alpha mattes generated using proposed algorithm is shown in Fig. 2e. Fig. 2Open in figure viewerPowerPoint Video matting results (a) Input frame, (b) Object tracking, (c) Automatically generated trimap, (d) Overlapping of trimap with input frame (to show accuracy of generated trimap), (e) Alpha matte generated using proposed technique 4 Simulation results The proposed video matting approach is evaluated visually and quantitatively on a manual data set shown in Fig. 3. The data set consists of 50 aircraft videos taken from different internet sources [59-61]. The aircraft videos are of varying length, that include the frames containing the primary object, i.e. aircraft and removed the frames where no obvious primary object is present. The experiments are performed on frames in total. The video sequences contain variety of aircrafts with different speed and pose variations as well as varying background complexity. For each video sequence, the ground truths (GTs) for key frames (1, 5, 10, 15, …) are generated manually by adopting the method described in [62]. The simulations are performed on laptop having CPU i7-8550U (1.80 GHz) and 16-GB RAM and MATLAB R-2015a. Fig. 3Open in figure viewerPowerPoint Sequences from manual aircraft videos data set The proposed technique is compared visually and quantitatively with existing state-of-the-art techniques including saliency-aware geodesic segmentation (SGS) [63], super-pixel level graph and spatiotemporal propagation (SGSP) [64] and bilateral space segmentation (BSS) [65]. SGS [63] and SGSP [64] incorporate spatial and temporal information at super-pixel level, while BSS [65] operates in bilateral space to segment the object from video sequence. Quantitative evaluation is performed by comparing output segmented maps with GTs using mean square error (MSE), precision, recall and f -measure. An evaluation of time complexity is also presented in Table 1. Table 1. Average quantitative evaluation Measures Sequences Techniques SGS [63] SGSP [64] BSS [65] Proposed MSE Sequence 1 839.103 847.526 12.405 7.184 Sequence 2 62.394 27.692 Sequence 3 620.108 786.926 32.938 21.692 Sequence 4 646.774 50.504 16.768 Sequence 5 65.127 42.116 average of 50 sequences 98.872 75.568 precision Sequence 1 0.7028 0.6239 0.8789 0.8998 Sequence 2 0.7819 0.6822 0.8587 0.8635 Sequence 3 0.6354 0.4523 0.8836 0.8721 Sequence 4 0.8019 0.4758 0.8415 0.8605 Sequence 5 0.4781 0.3075 0.6242 0.6848 average of 50 sequences 0.5820 0.4438 0.7837 0.8161 recall Sequence 1 0.8987 0.2035 0.8634 0.9059 Sequence 2 0.8202 0.2735 0.5908 0.8965 Sequence 3 0.5304 0.1547 0.7134 0.8458 Sequence 4 0.6905 0.0952 0.6338 0.7942 Sequence 5 0.7438 0.1158 0.6158 0.4953 average of 50 sequences 0.6736 0.1058 0.6944 0.7284 F-measure Sequence 1 0.7887 0.3089 0.8710 0.9058 Sequence 2 0.7988 0.3705 0.6974 0.8753 Sequence 3 0.5781 0.2305 0.7894 0.8587 Sequence 4 0.7375 0.1522 0.7188 0.8234 Sequence 5 0.5160 0.1622 0.6131 0.5493 average of 50 sequences 0.6245 0.1709 0.7364 0.7698 average time per frame (s) 10.557 9.876 4.96 3.34 The video sequence shown in Fig. 4 contains various rotation and scale variations of aircraft. The video contains 375 frames with frame size of . It can be seen in Fig. 4 that SGS [63] and SGSP [64] give erroneous results and a lot of background region is also included in foreground. BSS [65] produces comparatively better results, however generates inaccurate segmentation results along the boundaries of the aircraft, whereas the proposed technique accurately segments the aircraft from video sequence and generates high quality alpha mattes. Fig. 4Open in figure viewerPowerPoint Visual comparison of example sequence 1 Fig. 5 shows the frames of a video sequence having variation in size, shape and pose of aircraft. The video consists of 290 frames with frame size of . The selected frames show the movement of aircraft at various positions. It can be seen in results that SGS [63] and SGSP [64] fail to segment aircraft from complex background. BSS [65] correctly segments the aircraft from background, however there are lot of errors in extracted region as shown in Fig. 5, row 4. The proposed technique produces more accurate and high quality alpha mattes as compared to other existing techniques. Fig. 5Open in figure viewerPowerPoint Visual comparison of example sequence 2 In Fig. 6, a video sequence of 417 frames with frame size of is processed, that includes an aircraft with enormous pose variations. As seen in Fig. 6 SGS [63] and SGSP [64] produce degraded maps. BSS [65] extracted aircraft with distorted boundaries and certain background artifacts. However, proposed technique accurately extracts aircraft from video sequence. Fig. 6Open in figure viewerPowerPoint Visual comparison of example sequence 3 Fig. 7 shows a takeoff sequence of aircraft with various changes in position, pose and size of the aircraft. The takeoff sequence consists of 235 frames with frame size of . It can be clearly seen that the existing techniques produce inaccurate segmentation with lots of background distortion and boundary errors, while proposed technique not only correctly segments the foreground from background region, but also generates high quality alpha mattes. Fig. 7Open in figure viewerPowerPoint Visual comparison of example sequence 4 Fig. 8 shows a video sequence of aircraft with lots of scale, rotation, shape and size variation. The sequence consists of 600 frames with frame size of . SGS [63] and SGSP [64] fail to extract aircraft accurately and include a lot of background region in foreground. BSS [65] contains some boundary errors around aircraft as well as background artifacts are introduced, however the proposed technique correctly extracts the aircraft with least amount of error. Fig. 8Open in figure viewerPowerPoint Visual comparison of example sequence 5 Table 1 lists the average quantitative results of proposed technique with existing techniques [63-65] to compare the quality of extracted maps. A comparison of MSE is performed where less error values indicate that the proposed aircraft extraction results are closer to the GT. BSS [65] also produces less MSE, though has small segmentation errors. However, the saliency aware methods [63, 64] fail to accurately extract the aircraft from video sequences, thus produces high MSE. To further compare the performance of proposed technique against existing techniques, three well known metrics including precision, recall and f -measure are used. The values are computed by generating binary segmented maps of each technique using a fixed threshold. The values of precision, recall and f-measure ranges between [0,1], where higher values represent better results. The values are shown in Table 1, that describes a reliable comparison of how accurate each method extract aircraft from frames. The results clearly show that the proposed technique outperforms other existing techniques, thus is more accurate. The lower values of [63, 64] show that the techniques are unable to extract the desired object, rather incorporate more information from surrounding region. The comparison of time complexity is challengingly, since different techniques have different code bases and level of optimisation. Table 1 presents average runtime per frame of each technique with laptop specifications described above. The proposed technique efficiently reduces the computational complexity by reducing the area to be processed and produces high quality alpha mattes. 5 Conclusion A video matting technique for automatic extraction of aircraft from video sequence is proposed. Trimaps are generated automatically using non-rigid image registration. 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