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Maintaining moderate economic growth targets (EGTs) is the key for local governments to effectively implement the “carbon peak and carbon neutrality” goals under the refreshed development pattern. Utilizing panel data of 276 prefecture-level cities in mainland China from 2010 to 2020, and employing methods such as intermediary and threshold models, this study empirically analyzes the internal mechanism of EGT’s impact on urban carbon productivity (UCP). Our findings demonstrate that: ① The overall EGT during the analyzed period is not conducive to improving UCP. This conclusion remains valid after a series of robustness tests. This effect is more pronounced in the central region and resource-based cities than in the east-west region and non-resource-based cities. ② EGT not only directly suppresses UCP but also exerts indirect negative impacts on UCP from three aspects: delaying the digital economy (DE), constraining financial expansion (FE), and hindering green technology innovation (GTI). This negative indirect effect is similar to or even surpasses the direct effect, suggesting that the internal relationship between EGT and “dual-carbon” goals should be re-evaluated from a new compound perspective. ③ EGT not only has a simple linear impact on UCP but also significantly exhibits a dynamic evolution pattern in inverted “U” shape. That is, as EGT continuously upgrades, a nonlinear impact on UCP emerges in the form of “promoting first, suppressing later”. This indicates that surpassing the “degree” limit for EGT will be detrimental to the improvement of UCP. This study broadens the scope of carbon productivity analysis by introducing a new perspective centered on EGT. The insights gleaned from this research offer valuable guidance for local governments to effectively manage economic growth expectations and promote the synchronized achievement of dual-carbon objectives.

Multi-object tracking aims to recover the object trajectories, given multiple detections in video frames. Object feature extraction and similarity metric are the two keys to reliably associate trajectories. In this paper, we propose the recurrent metric network (RMNet), a convolutional neural network-recurrent neural network-based similarity metric framework for the multi-object tracking. Given a reference object, the RMNet takes as input random positive and negative detections and outputs similarity scores over time. The RMNet handles the long-term temporal object variations and false object detections by its long–short memory units. With the scores from RMNet, we introduce a batch multiple hypothesis (BMH) strategy, a simple yet efficient data association method for the batch multi-object tracking. BMH generates a hypothesis tree for each object with a dual-threshold hypothesis generation approach and, then, selects the best branch (or hypothesis) for each object as the batch tracking result. Specially, we model the hypothesis selection as a 0–1 programming problem and introduce a reward function to re-find the objects in case of missing detection. We evaluate our RMNet and BMH strategy on several popular datasets: 2DMOT2015, PETS2009, TUD, and KITTI. We achieve a performance comparable or superior to those of the state-of-the-art methods.

Road Detection is a basic task in automated driving field, in which 3D lidar data is commonly used recently. In this paper, we propose to rearrange 3D lidar data into a new organized form to construct direct spatial relationship among point cloud, and put forward new features for real-time road detection tasks. Our model works based on two prerequisites: (1) Road regions are always flatter than non-road regions. (2) Light travels in straight lines in a uniform medium. Based on prerequisite 1, we put forward difference-between-lines feature, while ScanID density and obstacle radial map are generated based on prerequisite 2. According to our method, we construct an array of structures to store and reorganize 3D input firstly. Then, two novel features, difference-between-lines and ScanID density, are extracted, based on which we construct a consistency map and an obstacle map in Bird Eye View (BEV). Finally, the road region is extracted by fusing these two maps and refinement is used to polish up our outcome. We have carried out experiments on the public KITTI-Road benchmark, achieving one of the best performances among the lidar-based road detection methods. To further prove the efficiency of our method on unstructured road, the visual outcomes in rural areas are also proposed.

Aiming to address dense small object tracking, we propose an image-to-trajectory framework including tracking and detection, where Track-Oriented Multiple Hypothesis Tracking(TOMHT) is revised for tracking. Unlike common cases of multi-object tracking, merged detections and the greater number of objects make dense small object tracking a more challenging problem. Firstly, we handle frequent merged detections through the aspects of detection and hypothesis selection. To tackle merged detection, we revise Local Contrast Method(LCM) and propose a multi-appearance variant, which exploits tree-like topological information and realizes one threshold for one object. Meanwhile, one-to-many constraint is employed via the proposed extended 0-1 programming, which enables hypothesis selection to handle track exclusions caused by merged detections. Secondly, to alleviate the high complexity caused by dense objects, we consider batch optimization and more rigorous and precise pruning technologies. Specifically, we propose autocorrelation based motion score test and two-stage hypotheses pruning. Experimental results are presented to verify the strength of our methods, which indicates speed and performance advantages of our tracker.

In this paper, we present a robust text detection approach in natural images which is based on region proposal mechanism. A powerful low-level detector named saliency enhanced-MSER extended from the widely-used MSER is proposed by incorporating saliency detection methods, which ensures a high recall rate. Given a natural image, character candidates are extracted from three channels in a perception-based illumination invariant color space by saliency-enhanced MSER algorithm. A discriminative convolutional neural network (CNN) is jointly trained with multi-level information including pixel-level and character-level information as character candidate classifier. Each image patch is classified as strong text, weak text and non-text by double threshold filtering instead of conventional one-step classification, leveraging confident scores obtained via CNN. To further prune non-text regions, we develop a recursive neighborhood search algorithm to track credible texts from weak text set. Finally, characters are grouped into text lines using heuristic features such as spatial location, size, color, and stroke width. We compare our approach with several state-of-the-art methods, and experiments show that our method achieves competitive performance on public datasets ICDAR 2011 and ICDAR 2013.