A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles’ in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians.

Driven by the prominent thermal signature of humans and following the growing availability of unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on the detection and tracking of pedestrians using thermal infrared images recorded from UAVs. However, pedestrian detection and tracking from the thermal images obtained from UAVs pose many challenges due to the low-resolution of imagery, platform motion, image instability and the relatively small size of the objects. This research tackles these challenges by proposing a pedestrian detection and tracking system. A two-stage blob-based approach is first developed for pedestrian detection. This approach first extracts pedestrian blobs using the regional gradient feature and geometric constraints filtering and then classifies the detected blobs by using a linear Support Vector Machine (SVM) with a hybrid descriptor, which sophisticatedly combines Histogram of Oriented Gradient (HOG) and Discrete Cosine Transform (DCT) features in order to achieve accurate detection. This research further proposes an approach for pedestrian tracking. This approach employs the feature tracker with the update of detected pedestrian location to track pedestrian objects from the registered videos and extracts the motion trajectory data. The proposed detection and tracking approaches have been evaluated by multiple different datasets, and the results illustrate the effectiveness of the proposed methods. This research is expected to significantly benefit many transportation applications, such as the multimodal traffic performance measure, pedestrian behavior study and pedestrian-vehicle crash analysis. Future work will focus on using fused thermal and visual images to further improve the detection efficiency and effectiveness.

This research explores the inherent vulnerability of nonlinear vehicle platoons characterized by the oscillatory behavior triggered by external perturbations. The perturbation exerted on the vehicle platoon is regarded as an external force on an object. Following the mechanical vibration analysis in mechanics, this research proposes a vibration-theoretic approach that advances our understanding of platoon vulnerability from two aspects. First, the proposed approach introduces damping intensity to characterize vehicular platoon vulnerability, which divides platoon oscillations into two types, i.e., underdamped and overdamped. The damping intensity measures the platoon’s recovery strength in responding to perturbations. Second, the proposed approach can obtain the resonance frequency of a nonlinear vehicle platoon, where resonance amplifies platoon oscillation magnitude when the external perturbation frequency equals the platoon’s damping oscillation frequency. The main contribution of this research lies in the analytical derivation of the closed-form formulas of damping intensity and resonance frequency. In particular, the proposed approach formulates platoon dynamics under perturbation as a second-order non-homogeneous ordinary differential equation, enabling rigorous derivations and analyses for platoons with complicated nonlinear car-following behaviors. Through simulations built on real-world data, this paper demonstrates that an overdamped vehicle platoon is more robust against perturbations, and an underdamped platoon can be destabilized easily by exerting a perturbation at the platoon’s resonance frequency. The theoretical derivations and simulation results shed light on the design of reliable platooning control, either for human-driven or automated vehicles, to suppress the adverse effects of oscillations.

In this paper, a vehicle Re-ID framework which integrates image processing and traffic flow model is developed. First, the CNN network is applied for vehicle detection and tracking and extracting attribute recognition. Particularly, attributes including vehicle color, type, make, and Re-ID feature are extracted to derive a similarity matrix between upstream and downstream vehicles. However, solely using these features could not achieve satisfaction matching accuracy. Our testing only shows a moderate accurate of around 72.3%. To further improve the Re-ID rate, this paper integrates visual information with the well-known IDM car-following mode. In our framework, IDM is first used to estimate the arrival time window for each upstream vehicle; and then with this time window derive a filter matrix which set the similarity as 0 for the matching vehicles outside the time window. Combining similarity matrix and filter matrix, the new developed Re-ID framework improves the matching rate to 95.7%. Furthermore, the proposed framework can even help identify vehicles that may have changed lanes, overtaken vehicles or driven on a sideroad. Such information is certainly valuable for future research on performance measure, traffic control, and congestion mitigation. Considering the significance of the trajectory data to nowadays traffic control and management and popularity of today’s surveillance cameras, this research certainly will contribute to the improvement of arterial traffic performance measure and efficient control.