CAD: concatenated action descriptor for one and two person(s), using silhouette and silhouette's skeleton

IET Image ProcessingVolume 14, Issue 3 p. 417-422 Research ArticleFree Access CAD: concatenated action descriptor for one and two person(s), using silhouette and silhouette's skeleton M. Shujah Islam, M. Shujah Islam Department of Electronic Engineering & Information Science, University of Science and Technology of China, Hefei, Anhui, People's Republic of ChinaSearch for more papers by this authorMansoor Iqbal, Mansoor Iqbal Department of Electronic Engineering & Information Science, University of Science and Technology of China, Hefei, Anhui, People's Republic of ChinaSearch for more papers by this authorNuzhat Naqvi, Nuzhat Naqvi Department of Electronic Engineering & Information Science, University of Science and Technology of China, Hefei, Anhui, People's Republic of ChinaSearch for more papers by this authorKhush Bakhat, Khush Bakhat Office of Research Innovation and commercialization (ORIC), National University of Computer and Emerging Sciences, Islamabad, PakistanSearch for more papers by this authorM. Mattah Islam, M. Mattah Islam Department of Computer Science & IT, Sarhad University of Science and Information Technology, Peshawar, Khyber-Pakhtunkhwa, PakistanSearch for more papers by this authorShamsa Kanwal, Shamsa Kanwal Department of Electronic Engineering & Information Science, University of Science and Technology of China, Hefei, Anhui, People's Republic of ChinaSearch for more papers by this authorZhongfu Ye, Corresponding Author Zhongfu Ye yezf@ustc.edu.cn Department of Electronic Engineering & Information Science, University of Science and Technology of China, Hefei, Anhui, People's Republic of ChinaSearch for more papers by this author M. Shujah Islam, M. Shujah Islam Department of Electronic Engineering & Information Science, University of Science and Technology of China, Hefei, Anhui, People's Republic of ChinaSearch for more papers by this authorMansoor Iqbal, Mansoor Iqbal Department of Electronic Engineering & Information Science, University of Science and Technology of China, Hefei, Anhui, People's Republic of ChinaSearch for more papers by this authorNuzhat Naqvi, Nuzhat Naqvi Department of Electronic Engineering & Information Science, University of Science and Technology of China, Hefei, Anhui, People's Republic of ChinaSearch for more papers by this authorKhush Bakhat, Khush Bakhat Office of Research Innovation and commercialization (ORIC), National University of Computer and Emerging Sciences, Islamabad, PakistanSearch for more papers by this authorM. Mattah Islam, M. Mattah Islam Department of Computer Science & IT, Sarhad University of Science and Information Technology, Peshawar, Khyber-Pakhtunkhwa, PakistanSearch for more papers by this authorShamsa Kanwal, Shamsa Kanwal Department of Electronic Engineering & Information Science, University of Science and Technology of China, Hefei, Anhui, People's Republic of ChinaSearch for more papers by this authorZhongfu Ye, Corresponding Author Zhongfu Ye yezf@ustc.edu.cn Department of Electronic Engineering & Information Science, University of Science and Technology of China, Hefei, Anhui, People's Republic of ChinaSearch for more papers by this author First published: 30 January 2020 https://doi.org/10.1049/iet-ipr.2018.6437Citations: 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 onFacebookTwitterLinked InRedditWechat Abstract This study introduces an action descriptor that has the ability to perform human action recognition efficiently for one and two person(s). The authors' proposed descriptor computes information like motion, spatial–temporal, diversion with respect to the centroid, critical point and keypoint detection, whereas the existing approaches lack to address this information efficiently. Action descriptors are developed from signature-based optical flow, signature-based corner points and binary robust invariant scalable keypoints. These action descriptors are applied to silhouette and silhouette's skeleton frames. These aforementioned action descriptors lead to developing the concatenated action descriptor (CAD). In order to develop action descriptors, the reference video frame plays an important role. Weizmann (one person) and both clean and noise versions of SBU Kinect Interaction (two persons) datasets are used for the evaluation of their proposed descriptors. On the other hand, classifications are performed by using support vector machine. Experimental results demonstrate that CAD not only outperforms among the entire proposed descriptors, but also provides better performance as compared to state-of-the-art approaches. 1 Introduction Human action recognition (HAR) is the popular research field of computer vision from the past few decades [1]. HAR has a lot of applications in the field of intelligent surveillance [2], content-based video search [3, 4], human–computer interaction [5, 6], robotics [7], assisted living [8] and player actions recognition in games [9]. Generally, actions could be categorised into individual, combined and crowded classes. Individual action class is irrespective of what the other person is performing, for instance Weizmann and KTH datasets [10]. In a combined action class, a single action is observed by multiple actor's performances like SBU Kinect Interaction dataset [11]. Crowd action recognition is different as compared to earlier mentioned classes where everybody is performing his own action and HAR system focuses on one or any particular group action. Data-driven crowd analysis is an example of a crowd analysis dataset. Modern devices are powerful in computation so that we could focus on efficiency instead of descriptor size. For practical based scenario, there is a need to develop such an efficient system that has an ability to perform desired action detection for one and two persons (combined). Most of the HAR based schemes encode both the object temporal and shape information in an action sequence. We have proposed action descriptors which perform processing on silhouette and silhouette's skeleton (i.e. skeleton obtained from silhouette) frames containing the object. The first frame of video is the frame of a reference and plays a vital role in developing all-action descriptors. Our presented descriptors are using signature-based optical flow, corner points and binary robust invariant scalable keypoint (BRISK) features [12]. CAD is developed by using above-mentioned descriptors, in order to compute information like motion, diversion with respect to centroid, spatial–temporal information, critical point and keypoint detection. Satisfactory performance of CAD can be observed for action recognition of two generally used datasets, i.e. Weizmann (one person) and for clean and noise version of SBU Kinect Interaction (two persons) datasets. The classification is performed by using support vector machine (SVM) [13]. Experimental evaluation shows effectiveness and completeness of CAD against proposed descriptors and state-of-the-art approaches. In this paper, we contribute to the following aspects: To capture motion and displacement information, we use an optical flow action descriptor based on signature features between centroid (desired location) and optical flow points. Silent information of object critical points from the centroid, we developed corner point action descriptor which contains signature between centroid and corner points. BRISK descriptor possesses detect object keypoints. CAD is formed by using optical flow, corner point and BRISK features that have the ability to capture information like motion, diversion with respect to centroid, spatial–temporal information, critical point and keypoint detection. Frame of reference, silhouette and silhouette's skeleton has contributed to improving the system's performance. The rest of paper is arranged as follows. In Section 2, covered related techniques overview on HAR. The proposed action descriptors are described in Section 3, whereas Section 4 explained experiments and Section 5 talked about evaluation of CAD descriptor. Finally, results and discussion elaborated in Section 6 and Section 7 concludes the entire paper. 2 Related work Until recently, many researchers are using silhouettes data to have performed HAR. Dedeoğlu et al. [14] proposed a technique that initially classifies the foreground objects into different categories by matching with predefined templates and later performs the action recognition against object of altered kinds. Silhouettes can be obtained from depth images, extracted from depth capturing camera. Mostly, researchers perform HAR by using skeleton data. In [15], the authors present an end-to-end spatial and temporal attention model this uses discriminative joints of skeleton data, 3D skeleton and LSTM (long short-term memory) networks. Global context-aware attention LSTM for focusing on the informative joints on skeleton frame is introduced in [7]. According to [16] traditional human actions recognition methods model generally; the spatial structure and temporal dynamic of a human skeleton with hand-crafted features. Alternatively, Liu et al. [17] describe the spatio-temporal LSTM with trust gates for 3D HAR using 3D skeleton data and proposes a tree-structure based traversal method. Ji et al. [18] designate an interactive body part contrast mining method based on joint features for 8 interactive limb pair body parts for human interaction recognition. Regularised deep LSTM networks and skeleton data [19] is also another scheme that recognises human action efficiently. A relationship between various body parts using rigid body rotations and translations: transformations in 3D space [20] come under the relative 3D geometry-based skeletal representations. Different types of optical flow based descriptors are proposed in the literature. Optical flow detection has many advantages such as background subtraction for moving objects in computer vision [21]. The disadvantage complies that the algorithm would generally not perform better when spatial locations of a point change abruptly with greater spatial distance. Action recognition by using Lucas–Kanade optical flow method is a standard approach. Lucena et al. [22] define optical flow accumulated local histograms for HAR. Histograms of oriented optical flow (HOOF) and, Binet–Cauchy kernels of non-linear dynamical systems are using for said purpose. In [23], the authors represent a model that works for each video frame through HOOF and to recognise human actions by classifying HOOF time series. The purpose of a generalisation of the Binet–Cauchy kernels to non-linear dynamical systems whose output lies in a non-Euclidean space. Representing an action by motion curves generated by optical flow features and clustered using Gaussian mixture [24]. The authors in [25] perform action recognition by stacking the motion features, atoms and phrases. Object classification has performed through the centroid distance signature in [14]. In [11] body-pose features are employed using multiple instance learning-based techniques. The relation history image descriptor proposed in [26] can be extracted from multiple types of activities to simplify the classification steps. Action classification algorithm [27] based on finding maximum cliques of motion primitives on the weighted graphs of the motion primitive co-occurrence pattern. This method generates key poses through initial segmentation of action class for gestures that similar to numerous observations [28]. 3D shape context is used for action recognition and preference is given to fast-moving body parts using a Euclidean 3D distance transform [29]. El-Henawy et al. [30] perform HAR by using HOG3D and Smith–Waterman partial shape matching. Most corner detectors are sensitive not specifically to corners, but to local image regions which have a high degree of variation in all directions but it could well-defined position and can be robustly detected. Despite the fact that binary rotation is individual limitation of BRISK algorithm, but still it could maintain a robust position for keypoint detection, description and matching. The HAR is performed by integrating the average energy (structural, translation and rotational information), silhouette posture information and orientation features [31]. Compound and continuous motion segment descriptors are presented for low and high level spatial information about neighbouring interest points, respectively [32]. For action recognition, convolutional neural network is combined with conditional random fields [33]. Hierarchical dynamic Bayesian networks and their algorithms are divided into action recognition system learning stages [34]. 3 Proposed action descriptors We proposed action descriptors using silhouettes and silhouette's skeleton frames. For pictorial representation, we selected two random frames of Bend and Shaking Hand class from Weizmann and SBU Kinect Interaction datasets. Silhouettes frames of Weizmann and SBU Kinect Interaction datasets are displayed in Figs. 1a and 2a, respectively, while Figs. 3a and 4a present silhouette's skeleton of both datasets. Pre-processing and thresholding are used to extract skeleton's frames [35]. Descriptors utilise centroid, optical flow, corner points and BRISK. CAD is a combination of optical flow, corner point and BRISK descriptors. The reference (first) frame is important for our proposed descriptors. The detail is shown in the preceding subsections. Fig. 1Open in figure viewerPowerPoint Bend class of Weizmann dataset (a) Silhouette frame, (b) Centroid detected on Silhouette frame, (c) Optical flow points detected on silhouette frame, (d) Corner points detected on silhouette frame, (e) BRISK points detected on silhouette frame Fig. 2Open in figure viewerPowerPoint Shaking-hands class of SBU Kinect Interaction dataset (a) Silhouette frame, (b) Centroid detected on silhouette frame, (c) Optical flow points detected on silhouette frame, (d) Corner points detected on silhouette frame, (e) BRISK points detected on silhouette frame Fig. 3Open in figure viewerPowerPoint Bend class of Weizmann dataset (a) Skeleton obtained from silhouette frame, (b) Centroid detected on silhouette's skeleton, (c) Optical flow points detected on silhouette's skeleton, (d) Corner points detected on silhouette's skeleton, (e) BRISK points detected on silhouette's skeleton Fig. 4Open in figure viewerPowerPoint Shaking-hands class of SBU Kinect Interaction dataset (a) Skeleton obtained from silhouette frame, (b) Centroid detected on silhouette's skeleton, (c) Optical flow points detected on silhouette's skeleton, (d) Corner points detected on silhouette's skeleton, (e) BRISK points detected on silhouette's skeleton 3.1 Centroid calculation Geometrical centre of object pixels is estimated in centroid calculation, which provides the information of object diversion from mean position. Signature-based optical flow and corner point descriptors are generated by utilising centroid and video reference frames. The centroid is calculated at the reference frame of silhouette and silhouette's skeleton. The centroid of silhouette's frames is displayed (green) in Figs. 1b and 2b while Figs. 3b and 4b present silhouette's skeleton of both datasets. The centroids (, ) for video reference frame are calculated by using the following equations: (1) (2)and (3)where is the area of object contour given in (3). 3.2 Signature-based optical flow Optical flow determined the object motion information in signature-based optical flow descriptor. Lucas–Kanade algorithm [36] compute optical flow between video frames effectively. To obtain the horizontal u and vertical v optical flow points, the constraints of (4) have to be solved (4)where , and are spatio-temporal derivatives contain two unknown's u and v. To solve (4), optical flow assumed as constant where the frame is divided into smaller segments. A weighted least-square fit of the optical flow constraint equation is applied to each segment s of the frame by minimising (5)a window W is designed for emphasising the constraints that are nearer to each segment's centre. Equation (6) is obtained by minimising (5) (6)Optical flow is detected on both silhouette and silhouette's skeleton from each frame. The optical flow for silhouettes frames is shown in Fig. 1c for Weizmann and Fig. 2c for SBU Kinect Interaction datasets. Figs. 3c and 4c show the optical flow silhouette's skeleton for both datasets. Detected optical flow leads to develop signature-based optical flow descriptors, which calculate the distance between each detected optical flow point and the centroid. Calculated object motion information with respect to the centroid is to get an optical flow signature. An optical flow signature is developed to calculated object motion information with respect to the centroid. The distance signature between LK optical flow points and centroid computes independently for silhouette and silhouette's skeleton frames as shown in the following equation: (7)Distance -based feature vector is calculated for each video frame. Signature-based optical flow descriptor is obtained by placing an entire matrix of feature vectors. 3.3 Signature-based corner point In signature-based corner point descriptor, corner points are detected in each silhouette and silhouette's skeleton frame [37]. Detected corner points of silhouette's frames are shown in Figs. 1d and 2d, however Figs. 3d and 4d present silhouette's skeleton. Corner points contain the 8-neighbourhood change of pixels. Changed pixels are critical and important points for making descriptors. The distance signature for corner points along with the centroid is computed independently for both silhouette and silhouette's skeleton frames, shown in the following equation: (8)Distance -based feature vector is calculated for each frame. Signature-based corner point descriptor is obtained by placing an entire matrix of feature vectors. 3.4 BRISK (binary robust invariant scalable keypoints) A BRISK action descriptor that valued in keypoint detection, description and matching [12] individually. It detects pile up information about image representation and object recognition [38]. Silhouette's frames are shown in Figs. 1e and 2e meanwhile, Figs. 3e and 4e show silhouette's skeleton frames. The main features of the method are: Scale-space representation: The scale-space representation is acquired by down sampling of original image and organising the resulting images into octaves and intra-octaves. Keypoint detection: The keypoint detection for the BRISK algorithm is the same as the one from FAST (features-from accelerated segment-test) algorithm, which detects local maxima or minima as points that are brighter or darker than an arc of contiguous pixels around it. Keypoint description: The BRISK descriptor consists of a 512-bit array obtains by comparing point-to-point the intensity of samples taken along a rotated circular pattern around the keypoint. If the intensity of the first point is higher than second, the corresponding bit is set to 1; otherwise, it is set to 0. 3.5 CAD (concatenated action descriptor) CAD concatenates; signature-based optical flow, signature-based corner point and BRISK descriptors (as shown in Fig. 1) and regularly perform better counter to individual descriptors. Figs. 5-7 demonstrated CAD improved result by comparing its values with state-of-the-art descriptors, it also gives high performance among individual proposed descriptors as stated in Fig. 8. Fig. 5Open in figure viewerPowerPoint CAD formation Fig. 6Open in figure viewerPowerPoint Comparison of CAD with state-of-the-art approaches for Weizmann dataset Fig. 7Open in figure viewerPowerPoint Comparison of CAD with state-of-the-art approaches for SBU Kinect Interaction dataset Fig. 8Open in figure viewerPowerPoint Individual performance of mentioned descriptors (a) Weizmann dataset, (b) SBU Kinect Interaction (clean version) dataset, (c) SBU Kinect Interaction (noise version) dataset 4 Experiment We performed experiments on two datasets namely Weizmann and SBU Kinect Interaction. Pre-processing and thresholding are used to extract silhouette's skeleton frames. The centroid is calculated from the reference frame. Respected points are detected on both silhouette's and silhouette's skeleton using optical flow, corner points and BRISK. Feature vectors are formed by calculating distance between centroid and detected features. Concatenating entire descriptors formed CAD. The computational complexity comparison of our proposed descriptors is shown in Fig. 9. This complexity is explained by calculating the time individually for each descriptor like optical flow on Weizmann (skeleton) is fast, whereas BRISK on SBU (noise) silhouette is the slowest of all individual descriptors. In the case of the CAD descriptor the Weizmann (skeleton) is efficient and SBU (noise) silhouette is most time-consuming. The performance of descriptors is irrespective of the feature vector size. Fig. 9Open in figure viewerPowerPoint Computational complexity comparison Our system specification: Processor & Intel(R) Core(TM) i5-3450 CPU @ 3.10 GHz, 3.10 GHz, installed memory (RAM) & 16.0 GB and System type & 64-bit Operating System (Window 10) and ×64-based processor. 5 Evaluation Table 1 illustrates the accuracy comparison between CAD (proposed) with state-of-the-art approaches by utilising Weizmann dataset [10]. As shown in Fig. 6, Cheema et al. [39] descriptor has attained 91.6%, which is lowest among all eight state-of-the-art approaches, whereas Chaaraoui and Flórez-Revuelta [40] descriptor obtains 97.8% accuracy. The average performance of entire existing approaches is 95.19%. On the other hand, CAD achieved 98.3% accuracy which clearly an improvement for Weizmann dataset. CAD performance with respect to state-of-the-art approaches by exploiting SBU Kinect Interaction dataset [11] has shown in Table 2. Here, we evaluate the accuracy of proposed descriptor contrary against ten diverse approaches on same dataset. As displayed in Fig. 7, the approach by Yun et al. [11] attains 80.03% accuracy, which is the lowest among ten state-of-the-art approaches, whereas Liu et al. [7] descriptor attained 94.9% accuracy. The average accuracy of entire addressing schemes is 88.73%. Our proposed, CAD accomplished 95.6% accuracy, which clearly shows better efficiency against SBU Kinect Interaction dataset. Table 1. Comparison of CAD with state-of-the-art approaches for Weizmann dataset Method Accuracy, % Cheema et al. [39] 91.6 Liu et al. [33] 93.3 Chaudhry et al. [23] 94.4 Grundmann et al. [29] 94.6 Xiao and Song [34] 96.5 Vishwakarma et al.[31] 96.64 Wang et al. [32] 96.7 Chaaraoui and Flórez-Revuelta [40] 97.8 CAD 98.3 Table 2. Comparison of CAD with state-of-the-art approaches for SBU Kinect Interaction dataset Method Accuracy, % Yun et al. [11] 80.03 Li et al. [27] 83.9 Edwards and Xie [28] 83.9 Ji et al. [18] 86.9 Zhu et al. [19] 90.41 Song et al. [15] 91.51 Gori et al. [26] 93.08 Liu et al. [17] 93.3 Liu et al. [7] 94.9 CAD 95.6 Liu et al. [7, 17] performed HAR using skeleton data. In [31, 40], action descriptors are developed using silhouette frames. According to our finding silhouette and silhouette's skeleton frames are important for performing HAR. Spatial and temporal information are calculated in [32], while in [26] descriptors are developed using joints and depth frames. Down sides of proceedings schemes contain extra information of joints while depth frames hold noise. Proposed CAD not only calculates spatial and temporal information but also compute its diversion with respect to the centroid of reference frames. Keypoints are detected on both silhouette and silhouette's skeleton frames. All these facts prove that CAD effectively performed HAR task (demonstrated in Tables 1 and 2). 6 Results and discussion Action recognition has efficiently performed on extensively used datasets of Weizmann [10] and SBU Kinect Interaction [11], both clean and noise versions. The Weizmann dataset contains ten discrete classes while SBU Kinect Interaction dataset contains eight non-identical classes, where multiple users perform different action sequences. Six different types of descriptors have been proposed for action recognition. All descriptors detect on the basis of signature-based optical flow, signature-based corner points and BRISK. Described techniques are developed on the silhouette and silhouette's skeleton frames. In Figs. 10a–c, the result for each, individual descriptor has been drawn for both silhouette and skeleton, whereas classification has been performed by using SVM [13]. Fig. 10Open in figure viewerPowerPoint Performance accuracy of CAD (a) Weizmann dataset, (b) SBU Kinect Interaction (clean version) dataset, (c) SBU Kinect Interaction (noise version) dataset CAD provides the best performance with the highest accuracy because it computes useful information likes motion, diversion with respect to the centroid, spatial–temporal information, critical point and keypoint detection. The main reason of concatenating these descriptors is that they show the diversity that if one descriptor lags the other descriptor compensates it, as shown in Fig. 6. Confusion matrices illustrate the performance of CAD for Weizmann and SBU Kinect Interaction (clean and noise) datasets as shown in Figs. 8a–c, respectively. According to these matrices, the average performance of CAD is 96.27%. On the other hand, in Figs. 10a–c, respectively, show the average performance accuracy against both mentioned datasets of other proposed descriptors. BRISK silhouette's skeletons descriptor with 91.37% performance accuracy remains on top among the rest of action descriptors except CAD, whereas BRISK descriptor on silhouette frame achieved performance accuracy of 89.44%. The performance accuracy gets through a corner point descriptor on silhouette's skeletons data remnant at 73.68%, while signature-based optical flow descriptor on silhouette secure only accuracy of 60.24%. In addition, signature-based optical flow descriptor on skeleton data and a corner point descriptor on silhouette frame obtains the performance accuracy 59.28 and 59.03%, respectively. Note that the size reduction gives extra advantage for the proposed signature-based optical flow descriptor. Fig. 8a shows the qualitative performance of the proposed CAD on Weizmann dataset and secured 98.3% values. The proposed descriptor achieved 95.2% values for bend class which lowest, however, run and jump class acquired 99.5%, respectively. Additionally, Fig. 8b shows the SBU Kinect Interaction (clean version) dataset and gained 95.6% values. The proposed descriptor achieved 93.3% values for kicking class which bottom, however, hugging class attained 97.3%, respectively. SBU Kinect Interaction (noise version) dataset and secured 94.7% values as shown in Fig. 8c. The proposed descriptor achieved 91.5% values for kicking class which bottommost however, hugging class achieved 96.7%, respectively. It is obvious to observe by experimental results that CAD contributes high performance accuracy among entire implemented action descriptors. Which clearly shows that if any descriptor lags for any class/dataset the other descriptor compensate performance (i.e. Wave 2 class of Weizmann dataset and Kicking class of SBU Kinect Interaction dataset). Hence, we can conclude that CAD is a complete descriptor for performing HAR task because of its diversity and better efficiency. 7 Conclusion This paper has proposed action descriptors for one and two person(s) by utilising Weizmann and SBU Kinect Interaction (clean and noise versions) datasets. These descriptors are applied on silhouette and skeletons obtained from silhouette frames. Novel CAD is formed by using descriptors obtained from signature-based optical flow, corner point and BRISK feature. It has computed information likes motion, diversion with respect to centroid, spatial–temporal information, critical point and keypoint detection. Experimental results demonstrate that frame of reference (first frame) is important for making action descriptor. 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