Computer Vision for Primate Behavior Analysis in the Wild

Advances in computer vision as well as increasingly widespread video-based behavioral monitoring have great potential for transforming how we study animal cognition and behavior. However, there is still a fairly large gap between the exciting prospects and what can actually be achieved in practice today, especially in videos from the wild. With this perspective paper, we want to contribute towards closing this gap, by guiding behavioral scientists in what can be expected from current methods and steering computer vision researchers towards problems that are relevant to advance research in animal behavior. We start with a survey of the state-of-the-art methods for computer vision problems that are directly relevant to the video-based study of animal behavior, including object detection, multi-individual tracking, (inter)action recognition and individual identification. We then review methods for effort-efficient learning, which is one of the biggest challenges from a practical perspective. Finally, we close with an outlook into the future of the emerging field of computer vision for animal behavior, where we argue that the field should move fast beyond the common frame-by-frame processing and treat video as a first-class citizen.

Multi Sentence Description of Complex Manipulation Action Videos

Automatic video description requires the generation of natural language statements about the actions, events, and objects in the video. An important human trait, when we describe a video, is that we are able to do this with variable levels of detail. Different from this, existing approaches for automatic video descriptions are mostly focused on single sentence generation at a fixed level of detail. Instead, here we address video description of manipulation actions where different levels of detail are required for being able to convey information about the hierarchical structure of these actions relevant also for modern approaches of robot learning. We propose one hybrid statistical and one end-to-end framework to address this problem. The hybrid method needs much less data for training, because it models statistically uncertainties within the video clips, while in the end-to-end method, which is more data-heavy, we are directly connecting the visual encoder to the language decoder without any intermediate (statistical) processing step. Both frameworks use LSTM stacks to allow for different levels of description granularity and videos can be described by simple single-sentences or complex multiple-sentence descriptions. In addition, quantitative results demonstrate that these methods produce more realistic descriptions than other competing approaches.

A Hierarchical Graph-based Approach for Recognition and Description Generation of Bimanual Actions in Videos

Nuanced understanding and the generation of detailed descriptive content for (bimanual) manipulation actions in videos is important for disciplines such as robotics, human-computer interaction, and video content analysis. This study describes a novel method, integrating graph based modeling with layered hierarchical attention mechanisms, resulting in higher precision and better comprehensiveness of video descriptions. To achieve this, we encode, first, the spatio-temporal inter dependencies between objects and actions with scene graphs and we combine this, in a second step, with a novel 3-level architecture creating a hierarchical attention mechanism using Graph Attention Networks (GATs). The 3-level GAT architecture allows recognizing local, but also global contextual elements. This way several descriptions with different semantic complexity can be generated in parallel for the same video clip, enhancing the discriminative accuracy of action recognition and action description. The performance of our approach is empirically tested using several 2D and 3D datasets. By comparing our method to the state of the art we consistently obtain better performance concerning accuracy, precision, and contextual relevance when evaluating action recognition as well as description generation. In a large set of ablation experiments we also assess the role of the different components of our model. With our multi-level approach the system obtains different semantic description depths, often observed in descriptions made by different people, too. Furthermore, better insight into bimanual hand-object interactions as achieved by our model may portend advancements in the field of robotics, enabling the emulation of intricate human actions with heightened precision.

Comparison of Motion Encoding Frameworks on Human Manipulation Actions

Movement generation, and especially generalisation to unseen situations, plays an important role in robotics. Different types of movement generation methods exist such as spline based methods, dynamical system based methods, and methods based on Gaussian mixture models (GMMs). Using a large, new dataset on human manipulations, in this paper we provide a highly detailed comparison of three most widely used movement encoding and generation frameworks: dynamic movement primitives (DMPs), time based Gaussian mixture regression (tbGMR) and stable estimator of dynamical systems (SEDS). We compare these frameworks with respect to their movement encoding efficiency, reconstruction accuracy, and movement generalisation capabilities. The new dataset consists of nine object manipulation actions performed by 12 humans: pick and place, put on top/take down, put inside/take out, hide/uncover, and push/pull with a total of 7,652 movement examples. Our analysis shows that for movement encoding and reconstruction DMPs are the most efficient framework with respect to the number of parameters and reconstruction accuracy if a sufficient number of kernels is used. In case of movement generalisation to new start- and end-point situations, DMPs and task parameterized GMM (TP-GMM, movement generalisation framework based on tbGMR) lead to similar performance and outperform SEDS. Furthermore we observe that TP-GMM and SEDS suffer from inaccurate convergence to the end-point as compared to DMPs. These different quantitative results will help designing trajectory representations in an improved task-dependent way in future robotic applications.

Simulated Mental Imagery for Robotic Task Planning

Traditional AI-planning methods for task planning in robotics require symbolically encoded domain description. While powerful in well-defined scenarios, setting this up requires substantial effort. Different from this, most everyday planning tasks are solved by humans intuitively, using mental imagery of the different planning steps. Here we suggest that the same approach can be used for robots, too, in cases which require only limited execution accuracy. In the current study, we propose a novel sub-symbolic method called Simulated Mental Imagery for Planning (SiMIP), which consists of several steps: perception, simulated action, success-checking and re-planning performed on 'imagined' images. We show that it is possible this way to implement mental imagery-based planning in an algorithmically sound way by combining regular convolutional neural networks and generative adversarial networks. With this method, the robot acquires the capability to use the initially existing scene to generate action plans without symbolic domain descriptions, hence, without the need to define an explicit representation of the environment. We create a dataset from real scenes for a packing problem of having to correctly place different objects into different target slots. This way efficiency and success rate of this algorithm could be quantified.

Infant movement classification through pressure distribution analysis -- added value for research and clinical implementation

In recent years, numerous automated approaches complementing the human Prechtl's general movements assessment (GMA) were developed. Most approaches utilised RGB or RGB-D cameras to obtain motion data, while a few employed accelerometers or inertial measurement units. In this paper, within a prospective longitudinal infant cohort study applying a multimodal approach for movement tracking and analyses, we examined for the first time the performance of pressure sensors for classifying an infant general movements pattern, the fidgety movements. We developed an algorithm to encode movements with pressure data from a 32x32 grid mat with 1024 sensors. Multiple neural network architectures were investigated to distinguish presence vs. absence of the fidgety movements, including the feed-forward networks (FFNs) with manually defined statistical features and the convolutional neural networks (CNNs) with learned features. The CNN with multiple convolutional layers and learned features outperformed the FFN with manually defined statistical features, with classification accuracy of $81.4\%$ and $75.6\%$, respectively. We compared the pros and cons of the pressure sensing approach to the video-based and inertial motion senor-based approaches for analysing infant movements. The non-intrusive, extremely easy-to-use pressure sensing approach has great potential for efficient large-scaled movement data acquisition across cites and for application in busy daily clinical routines for evaluating infant neuromotor functions. The pressure sensors can be combined with other sensor modalities to enhance infant movement analyses in research and practice, as proposed in our multimodal sensor fusion model.

Open video data sharing in developmental and behavioural science

Video recording is a widely used method for documenting infant and child behaviours in research and clinical practice. Video data has rarely been shared due to ethical concerns of confidentiality, although the need of shared large-scaled datasets remains increasing. This demand is even more imperative when data-driven computer-based approaches are involved, such as screening tools to complement clinical assessments. To share data while abiding by privacy protection rules, a critical question arises whether efforts at data de-identification reduce data utility? We addressed this question by showcasing the Prechtl's general movements assessment (GMA), an established and globally practised video-based diagnostic tool in early infancy for detecting neurological deficits, such as cerebral palsy. To date, no shared expert-annotated large data repositories for infant movement analyses exist. Such datasets would massively benefit training and recalibration of human assessors and the development of computer-based approaches. In the current study, sequences from a prospective longitudinal infant cohort with a total of 19451 available general movements video snippets were randomly selected for human clinical reasoning and computer-based analysis. We demonstrated for the first time that pseudonymisation by face-blurring video recordings is a viable approach. The video redaction did not affect classification accuracy for either human assessors or computer vision methods, suggesting an adequate and easy-to-apply solution for sharing movement video data. We call for further explorations into efficient and privacy rule-conforming approaches for deidentifying video data in scientific and clinical fields beyond movement assessments. These approaches shall enable sharing and merging stand-alone video datasets into large data pools to advance science and public health.

3D object reconstruction and 6D-pose estimation from 2D shape for robotic grasping of objects

We propose a method for 3D object reconstruction and 6D-pose estimation from 2D images that uses knowledge about object shape as the primary key. In the proposed pipeline, recognition and labeling of objects in 2D images deliver 2D segment silhouettes that are compared with the 2D silhouettes of projections obtained from various views of a 3D model representing the recognized object class. By computing transformation parameters directly from the 2D images, the number of free parameters required during the registration process is reduced, making the approach feasible. Furthermore, 3D transformations and projective geometry are employed to arrive at a full 3D reconstruction of the object in camera space using a calibrated set up. Inclusion of a second camera allows resolving remaining ambiguities. The method is quantitatively evaluated using synthetic data and tested with real data, and additional results for the well-known Linemod data set are shown. In robot experiments, successful grasping of objects demonstrates its usability in real-world environments, and, where possible, a comparison with other methods is provided. The method is applicable to scenarios where 3D object models, e.g., CAD-models or point clouds, are available and precise pixel-wise segmentation maps of 2D images can be obtained. Different from other methods, the method does not use 3D depth for training, widening the domain of application.

Combining optimal path search with task-dependent learning in a neural network

Finding optimal paths in connected graphs requires determining the smallest total cost for traveling along the graph's edges. This problem can be solved by several classical algorithms where, usually, costs are predefined for all edges. Conventional planning methods can, thus, normally not be used when wanting to change costs in an adaptive way following the requirements of some task. Here we show that one can define a neural network representation of path finding problems by transforming cost values into synaptic weights, which allows for online weight adaptation using network learning mechanisms. When starting with an initial activity value of one, activity propagation in this network will lead to solutions, which are identical to those found by the Bellman Ford algorithm. The neural network has the same algorithmic complexity as Bellman Ford and, in addition, we can show that network learning mechanisms (such as Hebbian learning) can adapt the weights in the network augmenting the resulting paths according to some task at hand. We demonstrate this by learning to navigate in an environment with obstacles as well as by learning to follow certain sequences of path nodes. Hence, the here-presented novel algorithm may open up a different regime of applications where path-augmentation (by learning) is directly coupled with path finding in a natural way.