Hyperspectral cameras can provide unique spectral signatures for consistently distinguishing materials that can be used to solve surveillance tasks. In this paper, we propose a novel real-time hyperspectral likelihood maps-aided tracking method (HLT) inspired by an adaptive hyperspectral sensor. A moving object tracking system generally consists of registration, object detection, and tracking modules. We focus on the target detection part and remove the necessity to build any offline classifiers and tune a large amount of hyperparameters, instead learning a generative target model in an online manner for hyperspectral channels ranging from visible to infrared wavelengths. The key idea is that, our adaptive fusion method can combine likelihood maps from multiple bands of hyperspectral imagery into one single more distinctive representation increasing the margin between mean value of foreground and background pixels in the fused map. Experimental results show that the HLT not only outperforms all established fusion methods but is on par with the current state-of-the-art hyperspectral target tracking frameworks.
@cite_26: When tracking small moving objects, primates use smooth pursuit eye movements to keep a target in the center of the field of view. In this paper, we propose the Smooth Pursuit tracking algorithm, which uses three kinds of saliency maps to perform online target tracking: appearance, location, and motion. In addition to tracking single targets, our method can track multiple targets with little additional overhead. The appearance saliency map uses deep convolutional neural network features along with gnostic fields, a brain-inspired model for object recognition. The location saliency map predicts where the object will move next. Finally, the motion saliency map indicates which objects are moving in the scene. We combine all three saliency maps into a smooth pursuit map, which is used to generate bounding boxes for tracked objects. We evaluate our algorithm and others from the literature on a vehicle tracking task. Our approach achieves the best overall performance, including being the only method we tested capable of handling long-term occlusions.
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@cite_7: Vehicle detection has been an important research field for years as there are a lot of valuable applications, ranging from support of traffic planners to real-time traffic management. Especially detection of cars in dense urban areas is of interest due to the high traffic volume and the limited space. In city areas many car-like objects (e.g., dormers) appear which might lead to confusion. Additionally, the inaccuracy of road databases supporting the extraction process has to be handled in a proper way. This paper describes an integrated real-time processing chain which utilizes multiple occurrence of objects in images. At least two subsequent images, data of exterior orientation, a global DEM, and a road database are used as input data. The segments of the road database are projected in the non-geocoded image using the corresponding height information from the global DEM. From amply masked road areas in both images a disparity map is calculated. This map is used to exclude elevated objects above a certain height (e.g., buildings and vegetation). Additionally, homogeneous areas are excluded by a fast region growing algorithm. Remaining parts of one input image are classified based on the Histogram of oriented Gradients (HoG)' features. The implemented approach has been verified using image sections from two different flights and manually extracted ground truth data from the inner city of Munich. The evaluation shows a quality of up to 70 percent.
@cite_3: Most tracking-by-detection algorithms train discriminative classifiers to separate target objects from their surrounding background. In this setting, noisy samples are likely to be included when they are not properly sampled, thereby causing visual drift. The multiple instance learning (MIL) paradigm has been recently applied to alleviate this problem. However, important prior information of instance labels and the most correct positive instance (i.e., the tracking result in the current frame) can be exploited using a novel formulation much simpler than an MIL approach. In this paper, we show that integrating such prior information into a supervised learning algorithm can handle visual drift more effectively and efficiently than the existing MIL tracker. We present an online discriminative feature selection algorithm that optimizes the objective function in the steepest ascent direction with respect to the positive samples while in the steepest descent direction with respect to the negative ones. Therefore, the trained classifier directly couples its score with the importance of samples, leading to a more robust and efficient tracker. Numerous experimental evaluations with state-of-the-art algorithms on challenging sequences demonstrate the merits of the proposed algorithm.
@cite_6: Modern aerial imaging platforms provide wide-area motion imagery (WAMI) at high spatial and moderate temporal resolutions making feasible a range of new applications. We consider the dual tasks of registering WAMI frames to geo-referenced vector road-maps and tracking vehicles through the progression of WAMI frames. We present a novel algorithm that performs these tasks jointly and offers improvements in both by exploiting the synergy between the tasks. Tracking for the large number of vehicles seen in urban-area WAMI is improved by auxiliary information that registration to the vector road-map provides by localizing roads within the scene. Similarly, registration of the WAMI frames to the vector map is improved by formulating the registration as a chamfer minimization between the vehicular trajectories and the road network, an approach that resolves challenges for registration posed by the fundamentally different data modalities between the aerial images and the vector road maps. Results obtained over our test datasets show the effectiveness of the proposed joint methodology. For both road network alignment and vehicle tracking, the proposed method offers a very significant improvement over available alternatives: the proposed approach yields better numerical metrics for quantification of registration accuracy and fewer false identification switches for tracked vehicles.
Detection, tracking and most recently, the counting of targets from satellite or aerial platform are useful for both commercial and government purposes. @cite_7 uses a disparity maps based approach with prior information of the road structure, orientation during image capture, and a global digital elevation model (DEM) to narrow down areas that can have vehicles. They then use an offline-trained histogram of oriented gradients (HoG) classifier for vehicle detection. @cite_3 proposed an online discriminative feature selection method that couples the classifier score with the importance of samples, leading to a more robust and efficient tracker. Yousefhussien, Browning and Kanan @cite_26 propose the Smooth Pursuit Tracking (SPT) algorithm which uses three kinds of saliency maps: appearance, location, and motion to track objects under all conditions, including long-term occlusion. Elliethy and Sharma @cite_6 proposed an innovative approach to register captured WAMI frames with vector road map data(Open Street Map @cite_4 ) and track vehicles within those registered frames simultaneously, leading to efficient results.