In this paper, we investigate the behaviour of current progress prediction methods on the currently used benchmark datasets. We show that the progress prediction methods can fail to extract useful information from visual data on these datasets. Moreover, when the methods fail to extract visual information, memory-based methods adopt a frame-counting strategy when presented with extsl{full-video} data as input. Additionally, we evaluate all the methods on a synthetic dataset we specifically designed for the progress prediction task. On our synthetic dataset the results show that all the methods can make use of the visual information and outperform the native, non-learning baselines. We conclude that in its current form the task of progress prediction is ill-posed. The learning methods tend to fail to extract useful information from the visual data and instead rely purely on frame counting.

How do automatic object detector outputs align with what humans consider good object detection? Our study is based on the responses of 70 participants for a survey. The participants are presented with images having bound- ing box predictions, their task is to choose images which according to them have an acceptable or a good detection. The results show a correlation between the size of the object and the evaluation metric IoU (Intersection over Union), with the size of the bounding box. Furthermore, the data indicates that the kind of box they prefer most for a detection output, is also the most accepted detection by them. Additionally, the results suggest that based on the symmetry of the object, position of the bounding box may or may not play a role for considering a detection valid. Our study investigates through human subjective choices if the traditional threshold value of IoU for evaluation, and tight bounding box outputs are always the best outputs in object detection techniques.

While the Routing Protocol for Low Power and Lossy Networks (RPL) is built to support the constraints of low-powered devices, it struggles to meet the standards in security. Generally, low-powered devices are challenged with limited cryptography, tough key management, and interoperability issues. Despite these concerns, security is not only deficient for RPL, but proposed mitigations appear untouched as well. This paper therefore contributes a lightweight cryptosystem. It questions and justifies its virtue in a twofold. First, we illustrate the importance of this mitigation with an impactful Sybil attack that enables malicious routing on root level. Second, we construct an attack pattern model and life-cycle to demonstrate the operational capabilities and objectives of the adversary for Internet of Things (IoT) generic attack patterns. The cryptosystem divides IEEE 802.15.4 networks into isolated clusters with key derivation functions. Because the key derivation function adopts spectrum resource measurements, the proposal includes a cooperative defence to validate these measurements of joining nodes. To avoid overhead, the mitigation operates on symmetric-key cryptography, piggybacked cluster identifiers, and Maximum Transmission Unit (MTU) requests to the trusted party that stores encrypted identity keys of member nodes. This mitigation, when combined with the efficient routing of RPL, enables a broad application for smart low-power constrained devices in a scalable IoT network while it protects against Sybil attacks and eavesdropping.

Routing Protocol for Low Power and Lossy Networks (RPL) is a routing protocol for Internet of Things (IoT) devices with limited resources. As IoT is becoming prevalent, it is important to secure the underlying protocols that compose it such as RPL. This paper sought to avoid an RPL-specific routing attack by modifying the protocol's functionalities. As a result, STIR is a novel method improving memory efficiency of routing tables in RPL's storing mode of operation to prevent Routing Table Overload attacks. STIR assigns IPv6 addresses in a specific way to form clusters of addresses, thus resulting in routing tables with sizes proportional to the node's number of sub-DODAGs. The proposed contribution increases memory efficiency in RPL's storing mode, therefore preventing routing table overload attacks. STIR necessitates few protocol modifications and additional control messages making it an ideal preventive method.

Regression is difficult because of noise, imbalanced data sampling, missing data, etc. We propose a method by classifying the continuous regression labels to tackle regression robustness problems. We analyze if our method can help regression, given that the class information is already included in the regression labels. We start by extensively experimenting on 1D synthetic datasets and find out that classification can help regression when the data sampling is i balanced. This happens when the data are clean, noisy in inputs and noisy in outputs, but not when they are partially missing. We then validate our conclusion on the KITTI dataset by estimating 3D object orientation. We conclude that our method can help regression in real-world.

A structured CNN filter basis allows incorporating priors about natural image statistics and thus require less training examples to learn, saving valuable annotation time. Here, we build on the Gaussian derivative CNN filter basis that learn both the orientation and scale of the filters. However, this Gaussian filter basis definition depends on a predetermined derivative order, which typically results in fixed frequency responses for the basis functions whereas the optimal frequency of the filters should depend on the data and the downstream learning task. We show that by learning the order of the basis we can accurately learn the frequency of the filters, and hence adapt to the optimal frequencies for the underlying task. We investigate the well-founded mathematical formulation of fractional derivatives to adapt the filter frequencies during training. Our formulation leads to parameter savings and data efficiency when compared to the standard CNNs and the Gaussian derivative CNN filter networks that we build on.

Person re-identification based on appearance is challenging due to varying views and lighting conditions in different cameras, or when multiple persons wear similar clothing styles and color. Considering these challenges, gait patterns provide an alternative to appearance, as gait can be captured from a distance and at a low resolution. In this paper we investigate and evaluate running gait as a unique attribute for video person re-identification in a recreational long-distance running event with 257 participants. We show that running gait recognition achieves competitive performance compared to video-based approaches in the cross-camera retrieval task and that gait and appearance features are complementary to each other. In addition, we compare gait recognition applied to walking and running sequences. An important difference is that we walk with straight arms, but run with bent arms. We propose to use human semantic parsing to create partial gait silhouettes from body parts to find the most discriminative combination. We demonstrate that the arm and leg swing are the most discriminative parts of the running gait. Our proposed method provides better recognition results by removing the torso from the silhouettes and allowing the arm swing to be more visible.

While sailing, sailors solely rely on their eyes to inspect the sail shape and adjust the sail configurations to achieve an appropriate sail shape that corresponds to the weather condition. This so-called trimming process requires years of experience. Hence the visual inspection of sail shape suffers from inaccuracy and communication difficulties. Therefore, this research proposes a visual analysis tool that presents an accurate sail shape structure and supports sailors in investigating the optimal sail shape of certain weather conditions. In order to achieve our goals, we reconstruct the sail shape using two triangulation methods. For incomplete reconstructed sail shapes caused by the uncertain image quality, we deform a complete template sail under the constraints of the incomplete sail shape and use the deformed sail as an estimation of the proper shape structure. We designed a visualization dashboard for sailors to explore the 3D structure, 2D profiles and characteristics of the time-varying sail shape and analyze their relation to boat speed. The usability of the visualization tool is tested by a qualitative evaluation with two sailing experts. The result shows that the reconstruction and deformation of sail shape are valid, and the visualization dashboard has the potential to enhance sailors' comprehension of sail shape and provide insights into the optimal sail shape trimming.

The Routing Protocol for Low-Power and Lossy Networks (RPL) has gained in popularity since the increased connectivity of everyday items to the Internet. One of the discovered attacks on RPL is the rank attack, which opens up possibilities for attackers to control traffic in the RPL network by spoofing their priority. Many solutions have been proposed to mitigate this attack over the past few years. There is no perfect solution yet, partly because the success of a mitigation is dependent on the network configuration in which it is implemented. In some network configurations,as this paper will show, common mitigation solutions are less effective. By selecting and analyzing four well-cited mitigation and detection solutions, the effectiveness of these proposals is reviewed when the network is configured to use nonlinear objective functions (NOFs). After this, a proposal is given to defend against a rank attack when using NOFs. TRAIL was proposed as a solution for preventing decreased rank attacks and uses a challenge-response mechanism to verify the path from a node to the root. This paper proposes T-TRAIL; an extended version of TRAIL that allows the measurement of downwards-trip-time to detect outliers in the network. By doing this, the rank attack can be prevented when the network uses a NOF. Finally, an estimation of the performance impact of T-TRAIL on the network is given based on the performance measurements of TRAIL.