رده بندی آنلاین از کارهای تصویری برای نظارت بر جریان کاری صنعتی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|21953||2011||9 صفحه PDF||سفارش دهید||7316 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Neural Networks, Volume 24, Issue 8, October 2011, Pages 852–860
Modelling and classification of time series stemming from visual workflows is a very challenging problem due to the inherent complexity of the activity patterns involved and the difficulty in tracking moving targets. In this paper, we propose a framework for classification of visual tasks in industrial environments. We propose a novel method to automatically segment the input stream and to classify the resulting segments using prior knowledge and hidden Markov models (HMMs), combined through a genetic algorithm. We compare this method to an echo state network (ESN) approach, which is appropriate for general-purpose time-series classification. In addition, we explore the applicability of several fusion schemes for multicamera configuration in order to mitigate the problem of limited visibility and occlusions. The performance of the suggested approaches is evaluated on real-world visual behaviour scenarios.We present a framework for online activity recognition in a complex industrial environment. ► We provide a novel method to automatically segment the input stream and classify segments. ► We propose GA–HMM: a hidden Markov model (HMM) combined with prior knowledge through a genetic algorithm (GA). ► GA–HMM outperforms an echo state network (ESN) in online recognition rates. ► Employing fusion schemes for multiple camera streams can improve accuracy.
Intelligent visual surveillance and classification of visual tasks are research fields that have rapidly gained momentum over recent years. Focusing on industrial plant smart monitoring, the aim is to recognise tasks happening in the scene, to monitor the smooth running of a workflow, and to detect any abnormal behaviour. Deviations from the workflow may cause severe deterioration of the quality of the product or may raise safety or security hazards. An example of such an industrial scenario is shown in Fig. 1. By monitoring industrial scenes, one faces several challenges such as recording data in work areas (camera positions and viewing area), industrial working conditions (sparks and vibrations), cluttered background (upright racks and heavy occlusion of the workers), high similarity of the individual workers (nearly all of them wearing a similar utility uniform), and other moving objects (welding machines and forklifts). Furthermore, the dynamics of the workflow can be quite complex. Several tasks within a workflow can have very different lengths and can be permutable. The high intraclass and low interclass variances make the classification process significantly challenging. Moreover, the tasks can include both human actions and motions of machinery in the observed process.Related work. Behaviour and workflow recognition has attracted the interest of many researchers. In the computer vision and machine learning communities, this is mainly addressed in applications such as abnormal behaviour recognition or unusual event detection. Many approaches have been suggested over recent years—reviews can be found in ( Poppe, 2010 and Turaga et al., 2008). Typically they build a model of normality, and the methods can differ in (i) the model used, (ii) the algorithm employed for learning the model parameters, and (iii) the features used. Models might be previously trained and kept fixed ( Antonakaki et al., 2009 and Wang et al., 2008) or adapt over time ( Breitenstein, Grabner, & Gool, 2009) to cope with changing conditions. A broad variety of extracted image features are used, such as global scene three-dimensional (3D) motion ( Padoy, Mateus, Weinland, Berger, & Navab, 2009) or object trajectories ( Antonakaki et al., 2009, Johnson and Hogg, 1996, Nguyen et al., 2005 and Shi et al., 2004), which require accurate detection and tracking. On the other hand, holistic methods, which define features at the pixel level and try to identify patterns of activity using them directly, can bypass the challenging processes of detection and tracking. Such methods may use pixel or pixel group features such as colour, texture, or gradient; see, for example, ( Zelnik-Manor & Irani, 2006) (histograms of spatiotemporal gradients) and ( Laptev & Perez, 2007) (spatiotemporal patches). Pixel change history (PCH) is used in ( Xiang & Gong, 2006) to represent each target separately after frame differencing. However, the representation of objects in PCH images is very simplistic (through ellipses), and cannot cope with realistic environments. A popular feature to use for action recognition is optical flow (see, e.g., Efros, Berg, Mori, & Malik, 2003), where a relatively small region of interest is extracted around a single human actor. In our case we need a much more efficient method, since our goal is online classification at high frame rates. Furthermore, in real applications, the targets may be partially occluded, so action recognition as defined in works such as ( Efros et al., 2003) would not be feasible. Various machine learning and statistical methods have been used for activity recognition, such as clustering (Boiman & Irani, 2005) and density estimation (Johnson & Hogg, 1996). A very popular approach is hidden Markov models (HMMs) (Ivanov and Bobick, 2000, Lv and Nevatia, 2006 and Padoy et al., 2009), due to the fact that they can efficiently model stochastic time series at various time scales. However, the HMMs assume that the input data are already segmented, an assumption which significantly limits their application in realistic applications. For this purpose, more complex HMM-based methods have been proposed such as hierarchical HMMs (HHMMs) (Fine et al., 1998 and Padoy et al., 2009) and layered HMMs (LHMMs) (Oliver, Garg, & Horvitz, 2004). However, the applicability of these methods assumes that the Markovian assumption holds for the tasks to be recognised, in other words the probability for the appearance of a task depends only on the previous one; this is not true in structured applications, where the execution of a task may influence the appearance of a series of following tasks. In such cases, the Markovian assumption would be an oversimplification, which would violate the application constraints. The use of higher-order models would result in very high complexity (Rabiner, 1989) and would raise issues such as “how many previous states do we have to consider?”. With a small number of tasks the problem could be still tractable; however, such approaches are not scalable to large numbers of tasks. In (Shi et al., 2004), the feasible task paths in a glycose calibration process were defined, using the so-called P-net to encode possible paths. The goals in our work are similar, but here we aim to show how to employ the HMM framework for recognising tasks in workflows, because of its very important extension possibilities (for example, with fusion (Zeng, Tu, Pianfetti, & Huang, 2008) or robustness (Chatzis, Kosmopoulos, & Varvarigou, 2009)); furthermore, we are going to encode possible paths as solutions provided by a genetic algorithm to cover a huge search space efficiently. An alternative approach to the HMM for the analysis of complex dynamical systems is echo state networks (ESNs) (Jaeger, 2001). ESNs offer several benefits, such as (i) fast and simple learning of many outputs simultaneously, (ii) the possibility of both offline and online learning, (iii) the capability of directly dealing with high-dimensional input data, and (iv) the ability to learn complex dynamic behaviours without any explicit Markovian assumption. On the other hand, there are two main limitations involved: (i) they can only recognise repetitive dynamics and (ii) all significant variations of task order in a given workflow have to be learnt to provide the best classification results. Previously, ESNs have been successfully used for time-series classification in speech recognition (Skowronski & Harris, 2007), human–robot interactions (Hellbach, Strauss, Eggert, Komer, & Gross, 2008), emotion recognition (Scherer, Oubbati, Schwenker, & Palm, 2008), and medicine (Verplancke et al., 2010). Recently, we examined the effectiveness of ESNs for workflow recognition from a single camera (Veres, Grabner, Middleton, & Gool, 2010). Nevertheless, the target visibility of specific tasks can be limited due to camera configuration and self-occlusions; therefore efficient ways to fuse observations from multiple cameras are necessary. Several fusion schemes for HMMs have been presented in the past, such as synchronous HMMs (Dupont & Luettin, 2000), parallel HMMs (Vogler & Metaxas, 1999), and multistream fused HMMs (Zeng et al., 2008). However, their applicability in multicamera systems has been examined only to a limited extent, for example in (Voulodimos, Grabner, Kosmopoulos, Van Gool, & Varvarigou, 2010), a previous work that is extended in this paper to address online behaviour and workflow recognition in continuous data streams, and in (Kosmopoulos & Chatzis, 2010), where offline classification of segmented sequences was examined. As far as ESNs are concerned, to our knowledge no fusion techniques have been employed for similar applications. Contribution. To our knowledge, no state-of-the-art tracking-based approach is able to cope with the significant particular challenges (as described above) of workflow analysis in continuous streams within industrial environments. We tried state-of-the-art methods for person detection/tracking ( Felzenszwalb et al., 2008 and Grabner and Bischof, 2006) 1; however, none of them showed stable and robust results in our industrial environment. Fig. 2(a) shows typical failures of the detector in our dataset, with a recall of 24% and a precision of only 9%. Thus, tracking-by-detection approaches (e.g., Huang, Wu, & Nevatia, 2008) cannot be used to generate trajectories. Also, the person could be hardly tracked, as displayed in Fig. 2(b). As for the tracker, it may start very well; however, it soon loses the person and drifts away.The reasons for the failures pertain to the nature of the environment, i.e., significant occlusions, clutter similar in structure/shape to a person, the workers coloured similarly to the racks, and unstable background due to welding flare, machinery operation, and lighting changes. Any of these in isolation would cause problems for person detection and tracking, but all of them together make the problem especially difficult for both detection and tracking, and prohibit the use of approaches based on trajectory analysis. Hence, we choose to use holistic features, which can be efficiently computed, do not rely on target detection and tracking, and can be used to model complex scenes (Veres et al., 2010). We contribute to the solution in the following ways. • We propose a novel method to automatically segment the input stream and to classify the resulting segments using prior knowledge and HMMs, combined through a genetic algorithm (GA). • We compare this approach to an online ESN-based method for time-series analysis of continuous streams. • We suggest using fusion schemes for multiple cameras to provide wider scene coverage, and to better cope with occlusions, thus improving the accuracy. The rest of this work is organised as follows. Section 2 formally defines the problem. In Section 3, we describe scene descriptors. Sections 4 and 5 describe the HMM-based fusion architectures and the proposed continuous stream segmentation method, while Section 6 presents the proposed GA–HMM that combines HMM classifications of the automatically segmented tasks and prior knowledge. In Section 7, the ESN-based approach addressing fusion is described. Section 8 is the experimental section, while Section 9 discusses the lessons learnt from our research and concludes the paper.
نتیجه گیری انگلیسی
In this paper, we have addressed the issue of online recognition of visual tasks and workflows in complex industrial environments. To this end the employment of holistic features based on a grid time matrix so as to bypass the challenging tasks of detection and tracking, which are usually unsuccessful in such environments, leads to a very satisfactory representation. We proposed the GA–HMM, which is an HMM endowed with a method to automatically segment the input stream and to exploit prior knowledge through a genetic algorithm. By doing so, we could take advantage of the versatile HMM architecture, for example, by incorporating elaborate fusion methods (Zeng et al., 2008) or robust models (Chatzis et al., 2009) for online stream classification. We scrutinised the effectiveness of this approach and compared it to an ESN-based approach. The GA–HMM approach outperformed the ESN, although the latter’s performance is influenced by the topological complexity and consequently the training time required. The ESN offers a simpler, more straightforward approach, which can yield satisfactory results when the training time can be compromised. A plus of the ESN is the automated segmentation of sequences, while GA–HMM relies on the ability to detect the task segments. Neither approach depends on the Markovian assumption to extract the sequences of tasks. However, as was recently shown, the ESN is practically influenced more by the most recent observations, so it is naturally expected to have more difficulties in classifying long sequences of tasks. Fusion of multiple camera streams provided added value in many cases. Between the fusion methods employed for both GA–HMM and ESN, the parallel fusion method exploited the redundancies between the different streams more effectively compared to feature fusion. The latter method assumes strict synchronisation, which is not the case in our setting. The benefits of fusion were more apparent in the ESN, where there was bigger room for improvement. Finally, the GA–HMM based on the multistream fused HMM could better capture interdependencies between streams and led to the highest recognition rates among all approaches. Finally, the proposed method can be easily employed in other workflows, simply by modifying the constraints of the solution given by the genetic algorithm accordingly, for example by allowing repetitions of tasks, omissions, etc. It is also scalable, because the underlying fusion methods are not limited by the number of streams.