Trajectory clustering is a cornerstone task in the field of trajectory mining. With the proliferation of deep learning, deep trajectory clustering has been widely researched to mine mobility patterns from massive unlabeled trajectories. Nevertheless, existing methods mostly ignore trajectories' temporal regularities, which are essential for mining fine-grained mobility patterns for applications including traveling group identification, transportation mode discovering, social security emergency, etc. To fill this gap, we propose ConDTC, a contrastive deep trajectory clustering method targeting for fine-grained mobility pattern mining. Specifically, we first design a spatial-temporal trajectory representation learning method which can capture both spatial and temporal regularities of trajectories synchronously. The proposed trajectory representation model can be used as a pre-trained model to serve various downstream trajectory mining tasks. Then, we construct a contrastive trajectory clustering module which optimizes trajectory representations and clustering performance simultaneously. Experimental results on three datasets validate that ConDTC can identify fine-grained mobility patterns by clustering trajectories with similar spatial-temporal mobility patterns together while separating those with different mobility patterns apart. Actually, ConDTC outperforms all state-of-the-art competitors substantially in terms of effectiveness, efficiency and robustness.
A frame level adaptive rate control scheme for the emerging High Efficiency Video Coding (HEVC) standard is proposed in this paper, where both rate model and distortion model are provided. For rate modeling, a new rate model is proposed based on the weighted complexity estimation of the previously encoded frames. For distortion modeling, the distortion is modeled as an exponential function of the sum of absolute transformed difference (SATD) and the quantization parameter of the current frame. Moreover, a quality smoothing method based on the distortion model is proposed to reduce the quality fluctuation. The proposed rate control algorithm is implemented into HM5.0. The proposed scheme can control the bitrate accurately with smoothing quality, and the coding gain compared with state-of-the-art technique is up to 0.64dB for LB HE & LB LC, 0.33dB, 0.31dB, 0.42dB, 0.44dB for LP HE, LP LC, RA HE and RA LC respectively.
Trajectory clustering is a cornerstone task in trajectory mining. Sparse and noisy trajectories like Call Detail Records (CDR) have become popular with the rapid development of mobile applications. However, existing trajectory clustering methods' performance is limited on these trajectories. Therefore, we propose a dual Self-supervised Deep Trajectory Clustering (SDTC) method, to optimize trajectory representation and clustering jointly. First, we leverage the BERT model to learn spatial-temporal mobility patterns and incorporate them into the embeddings of location IDs. Second, we fine-tune the BERT model to learn cluster-friendly representations of trajectories by designing a dual self-supervised cluster layer, which improves the intra-cluster similarities and inter-cluster dissimilarities. Third, we conduct extensive experiments with two real-world datasets. Results show that SDTC improves the clustering accuracy by 12.1% (on a noisy and sparse dataset) and 3.8% (on a very sparse dataset) compared with SOTA deep clustering methods.
In this paper, we propose a new frame level rate control algorithm for the high efficiency video coding (HEVC) based 3D video (3DV) compression standard. In the proposed scheme, a new initial quantization parameter (QP) decision scheme is provided, and the bit allocation for each view is investigated to smooth the bitrate fluctuation and reach accurate rate control. Meanwhile, a simplified complexity estimation method for the extended view is introduced to reduce the computational complexity while improves the coding performance. The experimental results on 3DV test sequences demonstrate that the proposed algorithm can achieve better R-D performance and more accurate rate control compared to the benchmark algorithms in HTM10.0. The maximum performance improvement can be up to 12.4% and the average BD-rate gain for each view is 5.2%, 6.5% and 6.6% respectively.
In the realm of human mobility data analysis, a multitude of constraints result in the publication of sparse, non-uniform implicit trajectories without explicit location information, such as coordinates. Researchers have dedicated substantial efforts towards trajectory recovery, aiming to densify trajectories and gain a more comprehensive understanding of human mobility. However, existing trajectory recovery methods focus on explicit trajectories, and require extensive historical data to capture users' mobility patterns. Nevertheless, implicit trajectories are usually more sparse than explicit trajectories. Addressing these challenges, we propose TrajBERT, an innovative BERT-based trajectory recovery method with spatial-temporal refinement. TrajBERT employs the Transformer encoder to learn mobility patterns bi-directionally and enhances the predictions by cross-stage temporal refinement. Subsequently, we design an output layer with global spatial refinement with a novel spatial-temporal aware loss function. To evaluate the performance of TrajBERT, we conduct a series of experiments on real-world datasets. Remarkably,TrajBERT yields at least 8.2% performance improvement compared to the state-of-the-art trajectory recovery approachs. Furthermore, TrajBERT successfully mitigates the cold start problem commonly experienced with new users lacking historical trajectories. It also shows superior robustness when faced with extremely sparse trajectories, thus demonstrating its potential as a practical tool in the field of human mobility analysis.
In this work, a CTU level rate control algorithm is proposed for High Efficiency Video Coding (HEVC). On top of the CTU level rate control algorithm, an efficient bit allocation method considering the HEVC hierarchical coding structure is specifically designed. Instead of directly applying the rate distortion model at the CTU level, it's proposed to derive the CTU level quantization parameter (QP) based on the frame level QP with feedback of the coding status of CTUs. To further improve the coding performance of the CTU level rate control, a QP adjustment strategy is proposed and incorporated into the rate control algorithm. Experimental results show that the proposed CTU level rate control algorithm can achieve better coding performance than the state-of-the-art rate control scheme for HEVC (1.9% for RA, 3.2% for LD). Moreover, the coding quality become much smoother with the proposed algorithm.
This paper proposes a coding tree unit (CTU) level rate control for HEVC based on the Laplace distribution modeling of the transformed residuals. Firstly, we give a study on the relationship model among the optimal quantization step, the Laplace parameter and the Lagrange multiplier. Based on the relationship model, the quantization parameter for each CTU can be dynamically adjusted according to the distribution of the transformed residual. Secondly, a CTU level rate control scheme is proposed to achieve accurate rate control as well as high coding performance. Experimental results show that the proposed rate control scheme achieves better coding performance than the state-of-the-art rate control schemes for HEVC in terms of both objective and subjective quality.
Mobile positioning plays an essential role in smart city services. This paper proposes a fingerprint positioning framework based on massive Minimization of Drive Test (MDT) data to provide accurate and efficient city-scale positioning without additional equipment and measures. First, a multi-level fingerprint construction method is proposed using the Timing Advance (TA), Reference Signal Receiving Power (RSRP), and Reference Signal Receiving Quality (RSRQ) of the serving cell and neighboring cell. Then, an adaptive online fingerprint matching method is employed to extract and match online data fingerprints. Experiments show that the median positioning error is 29.97 meters with city-scale MDT data. It outperforms the reported accuracy of the state-of-the-art fingerprint positioning method.
Call Detail Record (CDR) data are generated by mobile user equipment while using mobile communication services. With the development of communication techniques, an urgent need to efficiently store the CDR trajectory data has arisen due to the large volume of the data. Based on CDR data analysis, a lossless CDR trajectory compression method is proposed in this paper. The method employs Huffman coding to compress the trajectory data. We evaluated the proposed method on a real CDR dataset. And experimental results show that the compressed data size reduces to 2.1% of the original trajectory data size without losing location information.
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