Video motion magnification is the task of making subtle minute motions visible. Many times subtle motion occurs while being invisible to the naked eye, e.g., slight deformations in muscles of an athlete, small vibrations in the objects, microexpression, and chest movement while breathing. Magnification of such small motions has resulted in various applications like posture deformities detection, microexpression recognition, and studying the structural properties. State-of-the-art (SOTA) methods have fixed computational complexity, which makes them less suitable for applications requiring different time constraints, e.g., real-time respiratory rate measurement and microexpression classification. To solve this problem, we propose a knowledge distillation-based latency aware-differentiable architecture search (KL-DNAS) method for video motion magnification. To reduce memory requirements and to improve denoising characteristics, we use a teacher network to search the network by parts using knowledge distillation (KD). Furthermore, search among different receptive fields and multifeature connections are applied for individual layers. Also, a novel latency loss is proposed to jointly optimize the target latency constraint and output quality. We are able to find smaller model than the SOTA method and better motion magnification with lesser distortions. https://github.com/jasdeep-singh-007/KL-DNAS.

This paper presents temporal behavior trees (TBT), a specification formalism inspired by behavior trees that are commonly used to program robotic applications. We then introduce the concept of trace segmentation, wherein given a TBT specification and a trace, we split the trace optimally into sub-traces that are associated with various portions of the TBT specification. Segmentation of a trace then serves to explain precisely how a trace satisfies or violates a specification, and which portions of a specification are actually violated. We introduce the syntax and semantics of TBT and compare their expressiveness in relation to temporal logic. Next, we define robustness semantics for TBT specification with respect to a trace. Rather than a Boolean interpretation, the robustness provides a real-valued numerical outcome that quantifies how close or far away a trace is from satisfying or violating a TBT specification. We show that computing the robustness of a trace also segments it into subtraces.Finally, we provide efficient approximations for computing robustness and segmentation for long traces with guarantees on the result.We demonstrate how segmentations are useful through applications such as understanding how novice users pilot an aerial vehicle through a sequence of waypoints in desktop experiments and the offline monitoring of automated lander for a drone on a ship. Our case studies demonstrate how TBT specification and segmentation can be used to understand and interpret complex behaviors of humans and automation in cyber-physical systems.

We present our tool for the segmentation of temporal behavior trees (TBT), a novel formalism for monitoring specifications. TBTs can be easily retrofitted to behavior trees, commonly used to program robotic applications. Our tool supports the robustness semantics of TBT and generates trace segmentations. In other words, given a TBT specification and a trace, it determines the optimal assignment of TBT nodes to sub-traces. To illustrate its application, we use the example of an autonomous ship deck landing. We showcase the user inputs required and demonstrate how the outputs can be interpreted to identify challenging task aspects, contributing to a comprehensive system analysis.

Video motion magnification makes subtle invisible motions visible, such as small chest movements while breathing, subtle vibrations in the moving objects etc. But small motions are prone to noise, illumination changes, large motions, etc. making the task difficult. Most state-of-the-art methods use hand-crafted concepts which result in small magnification, ringing artifacts etc. The deep learning-based approach has higher magnification but is prone to severe artifacts in some scenarios. We propose a new phase-based deep network for video motion magnification that operates in both domains (frequency and spatial) to address this issue. It generates motion magnification from frequency domain phase fluctuations and then improves its quality in the spatial domain. The proposed models are lightweight networks with fewer parameters (∼0.11M and ∼0. 05M). Further, the proposed networks performance is compared to the SOTA approaches and evaluated on real-world and synthetic videos. Finally, an ablation study is also conducted to show the impact of different parts of the network.

Video motion magnification provides information to understand the subtle changes present in objects for applications like industrial, healthcare, sports, etc. Most state-of- the-art (SOTA) methods use hand-crafted bandpass filters, which require prior information for the motion magnification, produces ringing artifacts, and small magnification etc. While others use deep-learning based techniques for higher magnification, but their output suffers from artificially induced motion, distortions, blurriness, etc. Further, SOTA methods are computationally complex, which makes them less suitable for real-time applications. To address these problems, we proposed deep learning based simple yet effective solution for motion magnification. The proposed method uses a feature sharing and appearance encoder for better motion magnification with fewer distortions, artifacts etc. Additionally, for reducing magnification of noise and other unwanted changes, proxy-model based training is proposed. A computationally lightweight model (~0.12 M parameters) is proposed along with the base model. The performance of the proposed models is tested qualitatively and quantitatively, with the SOTA methods. Results demonstrate the effectiveness of the proposed lightweight and base model over the existing SOTA methods.

Automated video-based applications are a highly demanding technique from a security perspective, where detection of moving objects i.e., moving object segmentation (MOS) is performed. Therefore, we have proposed an effective solution with a spatio-temporal squeeze excitation mechanism (SqEm) based multi-level feature sharing encoder-decoder network for MOS. Here, the SqEm module is proposed to get prominent foreground edge information using spatio-temporal features. Further, a multi-level feature sharing residual decoder module is proposed with respective SqEm features and previous output features for accurate and consistent foreground segmentation. To handle the foreground or background class imbalance issue, we propose a region of interest-based edge loss. The extensive experimental analysis on three databases is conducted. Result analysis and ablation study proved the robustness of the proposed network for unseen video understanding over SOTA methods.