Existing 3D single object trackers (SOTs) of a point cloud all apply downscaling when extracting features from points. This operation leads to a loss of spatial and structural information, degrading tracking performance of sparsely distributed and small-scale objects. To address this problem, a structure aware SOT of a point cloud is proposed. Specifically, the backbone network is combined with the auxiliary network to learn point-wise representations. During the training stage, the subsidiary network is used to perform additional tasks and supervisions, which guides the backbone network to extract discriminative structural features. During the inference stage, this network part is detached to meet a real-time requirement as well as to ensure the tracking accuracy. In addition, the impacts of the quantity setting of the input point cloud and re-initiation strategy are discussed; these are significant to the performance but have been ignored by former works. The experimental results show that the proposed method has a distinct improvement even if the tracked object is sparse and small scale.
In order to improve the efficiency of LiDAR point cloud object recognition and reduce the computational overhead, a new feature descriptor, Hemispheric Unique Shape Context (HUSC), is presented in this paper by using an improved neighborhood determination method. Firstly, the normal vector and tangent plane at key point are estimated and the local reference frame is established. Then a hemispherical neighborhood is constructed based on the tangent plane and divided into bins according to azimuth, polar angle and radial direction. Finally, the points in each bin are counted and the local feature descriptors of key points are obtained. HUSC feature descriptor can not only ensure the discriminability of descriptors, but also improve the efficiency of object recognition by reducing the number of free bins. Experiments on Bologna dataset and 3DMatch dataset show that HUSC feature descriptor with hemispheric neighborhood is robust to noise, occupying less memory and operating faster.
KEYWORDS: Signal to noise ratio, Target recognition, Acoustics, Feature extraction, Electronic filtering, Signal processing, Detection and tracking algorithms, Sensors, Principal component analysis, Defense technologies
Feature extraction based on Gammatone filterbank is more robust than that from Mel filterbank in underwater acoustic recognition. However, both conventional auditory features only represent the energy-based amplitude of the signal, and their performance decrease in low underwater SNR environments. Phase represented by instantaneous frequency (IF) may also contain some characteristics of the target. This paper proposes a novel fusion feature based on the outputs of Gammatone filters, in which an optimized algorithm of instantaneous frequency is given. Experiments employs Support Vector Machine (SVM) as the classifier and relative results indicate that significant performance gains can be obtained with instantaneous frequency information in low noise conditions.
Frequency-Modulated Continuous-Wave Synthetic Aperture Radar (FMCW SAR) is a promising compact remote imaging sensor. In this paper, a ground moving targets refocusing method is presented to provide FMCW SAR system with simultaneous moving targets indication application. This method is modified from range migration algorithm. To discriminate the target optimally, the concept of relative motion is utilized. The moving target is refocused like a fixed target. Its migrations both in the range and azimuth directions are completely compensated. Blind hypotheses of the relative velocities are used in the detection phase of moving targets. The step size between the hypotheses involves a trade-off between the computation load and detectability. In this paper, we determine the discretization based on the principle of stationary phase. The discretization reduces the computation burden and secures the detectability.
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