Pose‐driven human activity anomaly detection in a CCTV‐like environment

IET Image ProcessingVolume 17, Issue 3 p. 674-686 ORIGINAL RESEARCHOpen Access Pose-driven human activity anomaly detection in a CCTV-like environment Yuxing Yang, Yuxing Yang orcid.org/0000-0003-4415-8080 Intelligent Sensing and Communications Research Group, Newcastle University, UK Contribution: Methodology, Resources, Software, Validation, Visualization, Writing - original draftSearch for more papers by this authorFederico Angelini, Federico Angelini orcid.org/0000-0001-8333-656X Thales UK Limited, Reading, RG2 6GF UK Contribution: ​Investigation, Methodology, Resources, Validation, Writing - original draftSearch for more papers by this authorSyed Mohsen Naqvi, Corresponding Author Syed Mohsen Naqvi [email protected] orcid.org/0000-0002-1547-0908 Intelligent Sensing and Communications Research Group, Newcastle University, UK Correspondence Syed Mohsen Naqvi,Intelligent Sensing and Communications Research Group, Newcastle University, UK. Email: [email protected] Contribution: Data curation, Formal analysis, Funding acquisition, Project administration, Supervision, Writing - review & editingSearch for more papers by this author Yuxing Yang, Yuxing Yang orcid.org/0000-0003-4415-8080 Intelligent Sensing and Communications Research Group, Newcastle University, UK Contribution: Methodology, Resources, Software, Validation, Visualization, Writing - original draftSearch for more papers by this authorFederico Angelini, Federico Angelini orcid.org/0000-0001-8333-656X Thales UK Limited, Reading, RG2 6GF UK Contribution: ​Investigation, Methodology, Resources, Validation, Writing - original draftSearch for more papers by this authorSyed Mohsen Naqvi, Corresponding Author Syed Mohsen Naqvi [email protected] orcid.org/0000-0002-1547-0908 Intelligent Sensing and Communications Research Group, Newcastle University, UK Correspondence Syed Mohsen Naqvi,Intelligent Sensing and Communications Research Group, Newcastle University, UK. Email: [email protected] Contribution: Data curation, Formal analysis, Funding acquisition, Project administration, Supervision, Writing - review & editingSearch for more papers by this author First published: 22 October 2022 https://doi.org/10.1049/ipr2.12664AboutSectionsPDF 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 Human activity anomaly detection plays a crucial role in the next generation of surveillance and assisted living systems. Most anomaly detection algorithms are generative models and learn features from raw images. This work shows that popular state-of-the-art autoencoder-based anomaly detection systems are not capable of effectively detecting human-posture and object-positions related anomalies. Therefore, a human pose-driven and object-detector-based deep learning architecture is proposed, which simultaneously leverages human poses and raw RGB data to perform human activity anomaly detection. It is demonstrated that pose-driven learning overcomes the raw RGB based counterpart limitations in different human activities classification. Extensive validation is provided by using popular datasets. Then, it is demonstrated that with the aid of object detection, the human activities classification can be effectively used in human activity anomaly detection. Moreover, novel challenging datasets, that is, BMbD, M-BMbD and JBMOPbD, are proposed for single and multi-target human posture anomaly detection and joint human posture and object position anomaly detection evaluations. 1 INTRODUCTION Human Activity Anomaly Detection (HAAD) is a challenging research topic in computer version. HAAD provides a binary label that informs whether human activity is normal or abnormal. HAAD yields many applications for modern society, such as surveillance systems, assisted living, human-to-machine interaction, robotics, and sports analysis [1, 2]. The first challenge is that different datasets may have different definitions for anomalies. The same action in the different environment sometimes have different normality labels, such as walking-on-the-sidewalk is normal, but walking-on-grass is abnormal. Another challenge is that human activities are complex temporal sequences of mixed gestures performed with various styles, attitudes, viewpoints, and light conditions. Moreover, human bodies can assume many different positions preserving their overall appearance. Last but not least, detecting the beginning and end of the activities is not trivial and requires ad-hoc strategies [3, 4]. This work focuses on video-based human CCTV-like monitoring and surveillance. In particular, we focus on considering human actions that posture-level semantic labels can describe, for example, running, bending, jumping, hands-waving, and sitting. Similarly, we focus on those normal/abnormal movements that belong to the posture-level semantic information. For example, a person falling, running or fighting in a scene where these events do not normally happen is considered abnormal. Similarly, critical objects positioned or unexpectedly handled are considered as abnormal events. Therefore, this work presents a novel system that explicitly addresses different abnormal activities related to the posture-level and target object position semantic. 1 It is based on pose-driven dee p learning networks that rely on human pose detections and RGB data within target bounding boxes to perform Joint RGB-Pose based features extraction, classification and anomaly detection. Unlike the traditional generative methods for video anomaly detection [5-9], we propose a multimodal algorithm for detecting abnormal behaviours and abnormal objects position. Our system consists of three parallel processing branches. The first extracts body pose information using a pose estimation algorithm and convert it into high-level features that are further processed by a neural network. The second processes raw RGB data within target bounding boxes to compensate for first branch failures' and enrich motion description. The third branch runs an object detector to collect objects labels and positions. The three branches extract features that can feed conventional machine learning models for anomaly detection. Since branch one and two are pre-trained on human action recognition tasks, the proposed system can simultaneously detect body movements and object positions anomalies while providing human action labels as an additional description. This architecture focuses on single targets data. However, it is simple to generalise multiple human targets and objects by using a tracking system. Extensive simulations are provided to highlight the efficacy and robustness of the proposed system. Firstly, pose, and RGB data is used to train the body movement branches. After picking up the best model, we fuse the body movement branches with the object position branches for joint pose-RGB HAAD. Our proposed work relies on detected body landmarks, so the accuracy of extracted pose information is critical. Therefore, popular video datasets were considered for evaluation and selected depending on the quality of detected landmarks. Furthermore, performance evaluation is carried out on our three novel datasets, that is, the Body Movements based Dataset (BMbD), the Multi-target Body Movements based Dataset (M-BMbD) and the Joint Body Movements and Object Position based Dataset (JBMOPbD), specifically recorded within Intelligent Sensing Lab, Newcastle University. In particular, BMbD proposes single-target posture-related anomalies; M-BMbD considers multi- target posture-related based anomalies; JBMOPbD further considers single-target posture-related anomalies alongside multi-object position based anomalies. Comparative results over the state-of-the-art are also provided to highlight the efficacy of the proposed method. In principle, relying on RGB data, we can detect anomalies coming from any source, such as people, cars, explosions and traffic. Moreover, RGB based methods can work in the wild and low-resolution situations. However, this work provides evidence that autoencoders based on RGB data are suboptimal in the case of posture-level HAAD with limited training data. When the scene semantic is given and known, that is, human posture-level actions in a CCTV-like scene, RGB-based methods do not explicitly consider this additional contextual knowledge. Therefore, as already mentioned, this work proposes considering human pose-driven networks, as human poses are assumed to contain most of the valuable information for posture-based HAAD. We demonstrate that pose data can boost RGB-based networks for action classification and anomaly detection. On the other hand, RGB data can instead compensate poses data in action recognition tasks. In short, merely relying on human poses and object bounding boxes, as in this work, the recognition system is explicitly injected with refined, non-redundant knowledge, which leads to better performance. This work focuses on semi-supervised HAAD [10]. In other words, no abnormal instances are presented to the algorithm during the training phase to build up abnormal expectations. Therefore, the anomaly detection algorithm is trained on the most challenging case where only normal data is available. In summary, the contributions of this work are: The joint RGB data, skeleton and localisation information model for Human Activity Anomaly Detection (HAAD), which provides real-time action explanation of the human activity; Three novel datasets for challenging posture-related and object position HAAD in CCTV-like environments: BMbD, M-BMbD and JBMOPbD. Section 2 summarises related works. Section 3 presents the novel RGB-Pose based networks with the aid of prior object information for HAAD. In Section 4, Simulations and results are provided. Moreover, conclusions are summarised in Section 5. 2 RELATED WORK Human-related pose estimation Motivated by skeleton-based human action recognition, [11, 12] and human tracking [13], our work relies on popular pose detectors, as it is found to be advantageous for analyzing the no-grid data (body joints). In particular, the pose detector OpenPose [14] is exploited as a pre-processing step to extract 25 human keypoints from each target in all frames. Human poses represent the main human action learning sources for the proposed method. The results can decrease the influence of external factors related to videos' quality, such as background illumination, camera's viewpoints, and pedestrians' appearances. All pedestrians are represented by pose graphs where the nodes mean the corresponding joints and the edges mean the relations between two nodes. The pose detectors provide frame-by-frame data for a variety of human targets. However, humans need to be tracked [15] from one frame to another to obtain consistent target's identities. This step makes sure that data from two or more similar targets, such as multiple humans, are correctly grouped over time based on the tracked identity. Thus, this step is called pose estimation instead of pose detection. Human targets have to be identified in each frame since the deep learning algorithm has to consider the human behaviours in the temporal domain. RGB-based and pose-based anomaly detection State-of-the-art HAAD methods [16] mainly rely on RGB raw data to perform the task. In very recent work, Sultani et al. [6] presented a fully supervised RGB-based anomaly detection system. The authors defined multiple learning instances to estimate scores for each video frame, that is, measuring the abnormality rate. Thus, only frame-level anomalies are considered in these methods, neglecting any other semantic level, that is, pixel-level and target-level. In semi-supervised learning, an important role is played by autoencoder based methods. Hasan et al. [8] proposed a combination of CNN-based autoencoders to model the temporal evolution of HOG-HOF features. Autoencoder-based approaches exploit normal data for training. Therefore, testing clips that resemble training data will be reconstructed with low error, while deviations from the training data are expected to be reconstructed with higher error. Thus, it is possible to identify abnormalities by analyzing the reconstruction error. Hasan et al. also provide pixel-level anomaly detection. However, it still does not provide any insights into what the abnormal pixel would represent, whether a human target performing unexpected actions or a car crashing, explosions or other abnormal events [8]. Chong et al. [9] proposed a similar approach but opting for combinations of CNN and LSTM for both Spatio-temporal encoding and decoding. However, this work does not provide insights into the nature of the detected anomalies. The other recent approaches are provided [17, 18], where background subtraction methods are used as a pre-processing step of raw data. It ensures that the system can focus only on moving objects. However, background subtraction methods fail on cluttered background scenes and in the case of pan-tilt-zoom camera recording. As opposite, human pose detection is robust to these challenges. In [19], a pose-based HAAD is proposed, which is based on clustering-based soft-assignment and Dirichlet Process-based Mixture models. Similarly, in [20], a pose-based HAAD algorithm is presented, which leverages the MPED-RNN (single encoder dual decoder) architecture. However, both the algorithms in [19, 20] do not perform human body motion analysis. In contrast, deep-learning-based features are extracted from RGB and pose data in the proposed work, leading to simultaneous HAAD. Other remarkable works [21-26] share similar common limitations, such as the anomaly detection is performed at the pixel level, not considering which semantic region contains the pixel. In this work, the above-mentioned autoencoder-based methods in [8], and [9] have been implemented as state-of-the-art baselines for semi-supervised HAAD to allow performance comparison on the proposed datasets. In section 4, we demonstrate that these methods fail in detecting the anomalies contained in the proposed datasets. Object detection Object detection is always the core task in computer vision. An effective object detection algorithm can detect the key objects information from the whole frame in real-time. In our tasks, object detection captures objects' class bounding boxes to decide whether a position is based on abnormal activities. There are several popular object detector such as SSD [27], YOLOv3 [28] and Mask R-CNN [29]. SSD [27], YOLOv3 [28] have a faster processing speed but can only detect the objects' bounding boxes. In contrast, Mask R-CNN [29] can detect the objects' bounding boxes and contours at the same time. Considering the computational cost, we use the popular object detector SSD [27] to provide bounding boxes, that is, Region Of Interest (ROI), and labels for a wide range of objects. SSD is exploited to extract the critical object position from RGB data to be fed into the learning process for object position anomaly detection. 3 PROPOSED JOINT RGB-POSE AND OBJECT POSITION MODEL FOR HAAD This section proposes to exploit the models defined for HAAD. The proposed approach is bi-level, which means that it can detect two types of anomalies, that is, abnormal body movements and abnormal object positions. HAAD problem statement Let D = N ∪ T $\mathbb {D} = \mathbb {N} \cup \mathbb {T}$ a video dataset, where N $\mathbb {N}$ contains human action normal data while T $\mathbb {T}$ represents testing data, which contains either normal or abnormal human actions. In particular, let N = { s j } j = 1 n $\mathbb {N} = \lbrace {\mathbf {s}}_j\rbrace _{j=1}^n$ be the normal video data subset containing n clips of arbitrary time length, where s j ${\mathbf {s}}_j$ represents the j-th RGB video clip. Let T = v i , { g v i ( t | ϕ ) } ϕ = 1 d i = 1 m , $$\begin{equation} \mathbb{T}={\left\{{\mathbf{v}}_{i},{\{{g}_{{\mathbf{v}}_{i}}(t|\phi )\}}_{\phi =1}^{d}\right\}}_{i=1}^{m}, \end{equation}$$ (1)be the video testing dataset containing m clips, where v i ${\mathbf {v}}_i$ represents the i-th RGB video clip, g v i ( t | ϕ ) $g_{{\mathbf {v}}_i}(t|\phi )$ is the anomaly based ground truth given for the target identity index ϕ. In particular, g v i ( t | ϕ ) $g_{{\mathbf {v}}_i}(t|\phi )$ is a function that associates each frame t and target identity ϕ to a binary response,that is, normal/abnormal label, and it can be defined as follows: g v i ( t | ϕ ) = 0 , if ϕ performs normal action in t 1 , if ϕ performs abnormal action in t $$\begin{equation} {g}_{{\mathbf{v}}_{i}}(t|\phi )=\left\{ \def\eqcellsep{&}\begin{array}{ll} 0,& \textit{if}\;\phi \;\textit{performs}\;\textit{normal}\;\textit{action}\;\textit{in}\;t\\[6pt] 1, & \textit{if}\;\phi \;\textit{performs}\;\textit{abnormal}\;\textit{action}\;\textit{in}\;t \end{array} \right. \end{equation}$$ (2)Since D $\mathbb {D}$ can be a single or a multi-target dataset, the target identity is provided by a tracking mechanism operating over the target detections. Thus, the goal of this section is to propose a strategy to compute G v i ( t | ϕ ) ≈ g v i ( t | ϕ ) $G_{{\mathbf {v}}_i}(t|\phi ) \approx g_{{\mathbf {v}}_i}(t|\phi )$ for all i = 1 , ⋯ , m $i = 1,\dots ,m$ and ϕ, and simultaneously providing an explanation of the performed action, defined as L v i ( t | ϕ ) $L_{{\mathbf {v}}_i}(t|\phi )$ , in the form of an action label. In principle, action anomalies within T $\mathbb {T}$ might be due to the unexpected target's body movements or with an unexpected positioning/usage of contextual objects. Therefore, this work proposes an anomaly detection algorithm that can detect both anomalies (Figure 1). FIGURE 1Open in figure viewerPowerPoint Illustration of the studied problem and the motivation of the work Body movements based HAAD 3.2.1 Pose features extraction The first step consists of extracting pose-related information from RGB data. To this purpose, OpenPose [14] is exploited to detect body landmarks of visible targets. This detector can provide up to 25 body landmarks and detection confidence for each landmark. The target bounding box can be retrieved by the detected landmarks set. These trackers consider both target detections and target appearance provided by RGB data within the target's bounding box. In principle, since multiple targets can be visible in each frame, one of our current tracking systems [4, 15, 30] can be exploited for labelling each detection with a unique target identity index. However, for simplicity, a more straightforward tracking system only based on the target's ROI coordinates has been applied [31]. Therefore, the tracked landmark sequences are treated with the ActionXPose, spatio-temporal-attention mechanism [2]. This robust method can transform the landmark sequence into a meaningful multi-variate sequence that encodes spatial and temporal information. This method creates poses libraries for each training action and exploits them to encode meaningful features in the training phase. Therefore, the output of this step consists of features in the form of multi-variate time sequences, encoding different aspects of the motion, including the mutual relation between sub-body parts and full-body similarities with the learned libraries. 3.2.2 Proposed joint RGB-pose models This section aims to propose three models for Joint RGB-Pose based action classification. The main idea is to jointly learn from both RGB data and pose-related features explained in Section 3.2.1, imposing a collaborative deep learning structure. Theoretically, if I represents the aggregate information contained within RGB data, then the pose-related information P is such that P ⊂ I $P \subset I$ , because poses are in turn obtained from RGB data. Therefore, it is reasonable to argue that learning from RGB data is expected to give similar, if not superior, results to learning from poses. However, as shown in this work, this is not the case due to the deep learning limitations, for example, it does not allow to explicitly point the network's attention over the correct image regions according to the human perspective. We propose three joint approaches based on multimodal data and multimodal learning algorithms to highlight and overcome this limitation. The proposed approaches variety is needed to support our conclusions adequately. Therefore, we defined three Joint RGB-Pose models, named Model A, B and C, deploying novel combinations of commonly used and well-established approaches for video-based deep learning, such as ConvNets, Two-streams networks and 3DCNNs [32]. As shown in Figure 3, Model A, B and C share a typical parallel-learning structure, where pose-based and RGB-based information flow in two deep learning branches. However, the proposed structure is not based on two completely independent branches. Generally, independent branches are those where the final decision is made by fusing the information at the score level [33]. However, this approach prevents from considering dependency between modalities. For example, it is reasonable to expect that pose-based learning is particularly effective for posture-related actions. At the same time, RGB data can describe finer appearance-related details, supporting the action recognition, especially when poses are not informative enough. However, challenging viewpoints can produce incomplete or corrupted poses. Therefore, it might be convenient to trust more RGB data than poses in these particular cases. In contrast, when poses are clear and body joints detectable, poses might be more effective as an information source. In other words, it is required to discriminate data sources dependently. Therefore, inspired by information fusion in [34], we incorporate this inter-dependency by firstly processing RGB data and poses separately. Subsequently, features vectors are concatenated, followed by a Fully Connected layer (FC) to learn dependency between feature vectors. Due to backpropagation, the network can jointly optimize its weight according to the contribution of both modalities depending on the conditions. This learning structure is shown in Figure 3, where the three proposed methods are drawn. FIGURE 2Open in figure viewerPowerPoint Proposed pipeline for bi-level HAAD. Colour version is recommended FIGURE 3Open in figure viewerPowerPoint Joint RGB-Pose proposed models. In all models, Video RGB data is firstly pre-processed with OpenPose and ActionXPose to extract pose-related features to feed the pose-based learning branch performed by the MLSTM-FCN. In parallel, raw RGB data also feeds the RGB-branch, which is based on different structures depending on the model, that is, Model A, B and C. Feature vectors extracted by both branches are concatenated and subsequently transformed by an FC layer, before the loss computation. Colour version is recommended Alongside the above-mentioned typical learning structure, it is necessary to specify how to extract feature vectors from each modality. Regarding the pose-branch, Models A, B and C are based on the Multivariate Long Short-Term Memory and Fully Convolutional Network (MLSTM-FCN) network [35], as supported by our extensive evaluation in [2]. Regarding the RGB-branches, inspired by [32], three different networks are considered: CRNN RGB data is processed through four CNN and two Fully Connected (FC) layers to extract frame-level features. Subsequently, the LSTM is used to model the time dependencies of the features across frames. ResNetCRNN RGB data is processed by using ResNet152 [36], pre-trained on ILSVRC-2012-CLS dataset [37], to extract frame-level features. Subsequently, LSTM is used to model the time dependencies of the features across frames. 3DCNN RGB data is processed using two 3DCNN and 2 FC layers to extract frame-level features and model time dependencies. Therefore, Model A, B and C are defined as in Figure 3, combining the RGB-branches mentioned above with the Pose-branch based on MLSTM-FCN. All models concatenate the features provided by the two parallel branches, followed by the usual FC layer to transform the combined feature vector into a class-based binary vector. Therefore, the loss computes the error between predicted classes and the ground truth. A joint RGB-pose model (A, B or C) has been trained for Action classification on a pre-defined dataset (different than D $\mathbb {D}$ ), with the unique restriction to consider k frames length action sequences. Let M k $\mathcal {M}_k$ be this model. Let also consider that the RGB data associated with the action sequences only consist of the target's bounding box. Therefore, we should assume that, to some extent, the model M k $\mathcal {M}_k$ can also perform action classification on unseen datasets. In particular, let consider D $\mathbb {D}$ as the unseen dataset. Therefore, as shown in Figure 2, clips in N $\mathbb {N}$ can be processed in the training phase to extract Pose-Based Branch features (PBB). Thus, PBB features are exploited to train an SVM one-class model for semi-supervised anomaly detection [38]. Similarly, T $\mathbb {T}$ clips are processed in the testing phase to extract PBB features to be injected into the trained SVM model to determine whether the testing sequence should be considered normal or abnormal. Since each PBB feature vector corresponds to a certain time window [ t 1 , ⋯ , t k ] $[t_1,\dots , t_k]$ for a given target ϕ, therefore G v i B M ( [ t 1 , ⋯ , t k ] | ϕ ) $G^{BM}_{{\mathbf {v}}_i}([t_1,\dots , t_k]|\phi )$ is computed. The superscript B M $BM$ denotes that G is obtained considering pose-based body movement-related features. In principle, RGB-Based features (RBB) can also be exploited for the same task. However, we prove in Section 4.2.4 that RBB features are not as effective as PBB features. It is worth mentioning that, regardless of the human action labels included in the M k $\mathcal {M}_k$ training, M k $\mathcal {M}_k$ is always able to extract features from any and unseen k frames-length action sequence. Suppose the unseen sequence resembles one of the M k $\mathcal {M}_k$ training sequences. In that case, the resulting feature vector is such that the read-out FC layer can reasonably provide the expected action label L v i ( [ t 1 , ⋯ , t k ] | ϕ ) $L_{{\mathbf {v}}_i}([t_1,\dots , t_k]|\phi )$ . As opposite, if the unseen sequence does not resemble training data for M k $\mathcal {M}_k$ , the resulting action label will be not necessarily an accurate description of the performed action. Nevertheless, the SVM model can still be trained on those features, regardless of the action-related knowledge they are carrying. As the first necessary approximation, we assume that posture-related HAAD is primarily independent of the object position in this work. Therefore, in the next section, object position based HAAD is proposed as the additional and independent level of HAAD. Object position based HAAD In this section, the goal is to perform anomaly detection based on the relative position between the target ϕ and the contextual Objects Position (OP). Let assume that the dataset D $\mathbb {D}$ contain a given set of w key objects O (objects under monitoring, including the human target). For each given frame t, let Human target : ( ϕ x , ϕ y ) Other objects : ( x , y ) h , 1 , … , ( x , y ) h , J h h = 2 w $$\begin{equation} \def\eqcellsep{&}\begin{array}{ll} & {\textit{Human}\;\textit{target}{:}\;({\phi}_{x},{\phi}_{y})}\\[6pt] & {\textit{Other}\;\textit{objects}{:}\;{\left\{{(x,y)}_{h,1},\ldots ,{(x,y)}_{h,{J}_{h}}\right\}}_{h=2}^{w}}\end{array} \end{equation}$$ (3)be the positions for the objects in O, where J h $J_h$ is the number of occurrences of the h-th object in O. Therefore, anomalies might come from the unexpected mutual positions between different objects in O. Thus, for each human target ϕ and for each frame t, multiple position-based q ( t , p ) ⊂ R 2 w ${\mathbf {q}}(t,p) \subset \mathbb {R}^{2w}$ feature vectors can be defined, one for each occurrence, as follows: q ( t , p ) = [ ϕ x , ϕ y , x 2 , j 2 , y 2 , j 2 , … , x w , j w , y w , j w ] ∀ j 2 ∈ [ 1 , … , J 1 ] , … , ∀ j w ∈ [ 1 , … , J w ] $$\begin{align} & \mathbf{q}(t,p)=\left\{[{\phi}_{x},{\phi}_{y},{x}_{2,{j}_{2}},{y}_{2,{j}_{2}},\ldots ,{x}_{w,{j}_{w}},{y}_{w,{j}_{w}}]\right\}\nonumber\\ & \forall {j}_{2}\in [1,\ldots ,{J}_{1}],\ldots ,\forall {j}_{w}\in [1,\ldots ,{J}_{w}] \end{align}$$ (4)The advantage of this approach is that it allows straightforward generalization to multiple human targets and multiple occurrences of the same object type in the scene. Let the Object Position-based Branch (OBB) be the set of OP feature vectors, as in Figure 2, that is, q ( t , p ) ⊂ OBB ${\mathbf {q}}(t,p) \subset \text{OBB}$ for a given frame t and target ϕ. Similarly to the previous section, a one-class SVM model can be trained by using normal features OBB extracted from N $\mathbb {N}$ and tested