Target Occlusion Research Articles

YOLO-ESL: An Enhanced Pedestrian Recognition Network Based on YOLO

Pedestrian detection is a critical task in computer vision; however, mainstream algorithms often struggle to achieve high detection accuracy in complex scenarios, particularly due to target occlusion and the presence of small objects. This paper introduces a novel pedestrian detection algorithm, YOLO-ESL, based on the YOLOv7 framework. YOLO-ESL integrates the ELAN-SA module, designed to enhance feature extraction, with the LGA module, which improves feature fusion. The ELAN-SA module optimizes the flexibility and efficiency of small object feature extraction, while the LGA module effectively integrates multi-scale features through local and global attention mechanisms. Additionally, the CIOUNMS algorithm addresses the issue of target loss in cases of high overlap, improving boundary box filtering. Evaluated on the VOC2012 pedestrian dataset, YOLO-ESL achieved an accuracy of 93.7%, surpassing the baseline model by 3.0%. Compared to existing methods, this model not only demonstrates strong performance in handling occluded and small object detection but also remarkable robustness and efficiency.

Spatiotemporal Information, Near-Field Perception, and Service for Tourists by Distributed Camera and BeiDou Positioning System in Mountainous Scenic Areas

The collaborative use of camera near-field sensors for monitoring the number and status of tourists is a crucial aspect of smart scenic spot management. This paper proposes a near-field perception technical system that achieves dynamic and accurate detection of tourist targets in mountainous scenic areas, addressing the challenges of real-time passive perception and safety management of tourists. The technical framework involves the following steps: Firstly, real-time video stream signals are collected from multiple cameras to create a distributed perception network. Then, the YOLOX network model is enhanced with the CBAM module and ASFF method to improve the dynamic recognition of preliminary tourist targets in complex scenes. Additionally, the BYTE target dynamic tracking algorithm is employed to address the issue of target occlusion in mountainous scenic areas, thereby enhancing the accuracy of model detection. Finally, the video target monocular spatial positioning algorithm is utilized to determine the actual geographic location of tourists based on the image coordinates. The algorithm was deployed in the Tianmeng Scenic Area of Yimeng Mountain in Shandong Province, and the results demonstrate that this technical system effectively assists in accurately perceiving and spatially positioning tourists in mountainous scenic spots. The system demonstrates an overall accuracy in tourist perception of over 90%, with spatial positioning errors less than 1.0 m and a root mean square error (RMSE) of less than 1.14. This provides auxiliary technical support and effective data support for passive real-time dynamic precise perception and safety management of regional tourist targets in mountainous scenic areas with no/weak satellite navigation signals.

Railway foreign object tracking and detection with spatial positioning and feature generalization enhancement

The existing deep learning foreign object tracking and detection algorithm is easily affected by complex environments and target occlusion, resulting in problems such as missed detection and low detection accuracy. A railway foreign object tracking and detection algorithm with spatial positioning and feature generalization enhancement is proposed. First, a multi-scale cascade improved GhostNet feature network is proposed to improve the feature extraction capability of infrared targets. Secondly, the spatial positioning and feature generalization enhancement module is designed by using the spatial position positioning and generalized morphological information of foreign objects to enhance the detection accuracy of targets with position movement and tracking trajectory changesin complex scenes. Then, a pyramid prediction network is constructed to obtain the detection anchor frame, type and confidence information of infrared railway foreign objects. Finally, by improving the DeepSORT tracking algorithm with improved category and confidence display, combined with Kalman filtering and Hungarian algorithm, railway foreign object tracking and detection in infrared weak light environment is realized. Experimental results show that the proposed method has a tracking and detection accuracy of railway foreign objects 83.3%, with an average detection rate of 11.3 frames per second. Compared with the comparison method, the proposed method has higher detection accuracy and has better performance in railway foreign object tracking and detection in infrared weak light scenes.

Visual tracking algorithm based on template updating and dual feature enhancement

Aiming at the problem of tracking failure due to target deformation, flipping and occlusion in visual tracking, a template updating algorithm based on image structural similarity is proposed by dynamically updating the template to adapt to the changes of the target during tracking, specifically, a queue is used to save the recent N -frame tracking results, and a decision is made on whether to update the template or not based on the structural similarity score between the current tracking results and the template image, and if updated, the template is matched from the historical N -frame tracking results as the new template. If updated, the optimal target image is matched from the historical N -frame tracking results as a new template for subsequent tracking. The tracking feature enhancement module and segmentation feature enhancement module are also designed based on SiamMask network. The tracking feature enhancement module consists of non-local operations and convolutional downsampling, which is used to establish contextual correlation, enhance the target features, suppress the background interference, improve the tracking robustness, and solve the feature attenuation problem due to the occlusion of the target. The segmentation feature enhancement module introduces the convolutional block attention module and deformable convolution to improve the network's ability to capture channel and spatial features, adaptively learn the shape and contour information of the target, and enhance the network's segmentation accuracy of the tracked target. , which in turn improves the tracking accuracy. Experiments show that the proposed algorithm performs well and stably in solving the above problems, improving the expected average overlap rate by 5.2%, 5.3%, and 2.5%, and the robustness by 6%, 7.9%, and 15.6%on the VOT2016, VOT2018, and VOT2019 datasets , respectively, and achieving a real-time speed of 91 frames per second, when compared to the baseline SiamMask.

Enhancing Small Target Detection in Aerial Imagery with Bi-PAN-FPN and EDMOA-Optimized YOLOv8-s

Across the globe, people are working to build "smart cities" that will employ technology to make people's lives better and safer. Installing cameras at strategic spots across the city to monitor public spaces besides provide real-time footage to law enforcement besides other local authorities is a crucial part of smart city infrastructure, which includes video surveillance. A more effective answer is provided by deep learning algorithms, however research in this area still faces significant problems from changes in target size, form change, occlusion, and illumination circumstances as seen from the drone's perspective. In light of the aforementioned issues, this study presents a highly effective and resilient approach for aerial picture identification. To begin, the concept of Bi-PAN-FPN is presented to enhance the neck component of YOLOv8-s, taking into consideration the prevalent issue of small targets being easily misdetected or ignored in aerial photos. We achieve a more advanced and thorough feature fusion procedure much as feasible by completely considering and reusing multiscale features. To further reduce the amount of parameters in the model and prevent info loss during long-distance feature transfer, the benchmark model's backbone incorporates the GhostblockV2 structure in lieu of a portion of the C2f module. With the help of the Enhanced Dwarf Mongoose Optimization Algorithm (EDMOA), the suggested model's hyper-parameters are optimised. Lastly, a dynamic nonmonotonic focusing mechanism is employed in conjunction with WiseIoU loss as bounding box regression loss. The detector accounts for varying anchor box quality by utilizing "outlier" evaluations, thus improving the complete presentation of the detection task.

Research on a Metal Surface Defect Detection Algorithm Based on DSL-YOLO

In industrial manufacturing, metal surface defect detection often suffers from low detection accuracy, high leakage rates, and false detection rates. To address these issues, this paper proposes a novel model named DSL-YOLO for metal surface defect detection. First, we introduce the C2f_DWRB structure by integrating the DWRB module with C2f, enhancing the model’s ability to detect small and occluded targets and effectively extract sparse spatial features. Second, we design the SADown module to improve feature extraction in challenging tasks involving blurred images or very small objects. Finally, to further enhance the model’s capacity to extract multi-scale features and capture critical image information (such as edges, textures, and shapes) without significantly increasing memory usage and computational cost, we propose the LASPPF structure. Experimental results demonstrate that the improved model achieves significant performance gains on both the GC10-DET and NEU-DET datasets, with a mAP@0.5 increase of 4.2% and 2.6%, respectively. The improvements in detection accuracy highlight the model’s ability to address common challenges while maintaining efficiency and feasibility in metal surface defect detection, providing a valuable solution for industrial applications.

Synchronous End-to-End Vehicle Pedestrian Detection Algorithm Based on Improved YOLOv8 in Complex Scenarios.

In modern urban traffic, vehicles and pedestrians are fundamental elements in the study of traffic dynamics. Vehicle and pedestrian detection have significant practical value in fields like autonomous driving, traffic management, and public security. However, traditional detection methods struggle in complex environments due to challenges such as varying scales, target occlusion, and high computational costs, leading to lower detection accuracy and slower performance. To address these challenges, this paper proposes an improved vehicle and pedestrian detection algorithm based on YOLOv8, with the aim of enhancing detection in complex traffic scenes. The motivation behind our design is twofold: first, to address the limitations of traditional methods in handling targets of different scales and severe occlusions, and second, to improve the efficiency and accuracy of real-time detection. The new generation of dense pedestrian detection technology requires higher accuracy, less computing overhead, faster detection speed, and more convenient deployment. Based on the above background, this paper proposes a synchronous end-to-end vehicle pedestrian detection algorithm based on improved YOLOv8, aiming to solve the detection problem in complex scenes. First of all, we have improved YOLOv8 by designing a deformable convolutional improved backbone network and attention mechanism, optimized the network structure, and improved the detection accuracy and speed. Secondly, we introduced an end-to-end target search algorithm to make the algorithm more stable and accurate in vehicle and pedestrian detection. The experimental results show that, using the algorithm designed in this paper, our model achieves an 11.76% increase in precision and a 6.27% boost in mAP. In addition, the model maintains a real-time detection speed of 41.46 FPS, ensuring robust performance even in complex scenarios. These optimizations significantly enhance both the efficiency and robustness of vehicle and pedestrian detection, particularly in crowded urban environments. We further apply our improved YOLOv8 model for real-time detection in intelligent transportation systems and achieve exceptional performance with a mAP of 95.23%, outperforming state-of-the-art models like YOLOv5, YOLOv7, and Faster R-CNN.

SimpleTrackV2: Rethinking the Timing Characteristics for Multi-Object Tracking.

Multi-object tracking tasks aim to assign unique trajectory codes to targets in video frames. Most detection-based tracking methods use Kalman filtering algorithms for trajectory prediction, directly utilizing associated target features for trajectory updates. However, this approach often fails, with camera jitter and transient target loss in real-world scenarios. This paper rethinks state prediction and fusion based on target temporal features to address these issues and proposes the SimpleTrackV2 algorithm, building on the previously designed SimpleTrack. Firstly, to address the poor prediction performance of linear motion models in complex scenes, we designed a target state prediction algorithm called LSTM-MP, based on long short-term memory (LSTM). This algorithm encodes the target's historical motion information using LSTM and decodes it with a multilayer perceptron (MLP) to achieve target state prediction. Secondly, to mitigate the effect of occlusion on target state saliency, we designed a spatiotemporal attention-based target appearance feature fusion (TSA-FF) target state fusion algorithm based on the attention mechanism. TSA-FF calculates adaptive fusion coefficients to enhance target state fusion, thereby improving the accuracy of subsequent data association. To demonstrate the effectiveness of the proposed method, we compared SimpleTrackV2 with the baseline model SimpleTrack on the MOT17 dataset. We also conducted ablation experiments on TSA-FF and LSTM-MP for SimpleTrackV2, exploring the optimal number of fusion frames and the impact of different loss functions on model performance. The experimental results show that SimpleTrackV2 handles camera jitter and target occlusion better, achieving improvements of 1.6%, 3.2%, and 6.1% in MOTA, IDF1, and HOTA, respectively, compared to the SimpleTrack algorithm.

Underwater target detection network based on differential routing assistance and bilateral attention synergy

Underwater target detection technology holds significant importance in both military and civilian applications of ocean exploration. However, due to the complex underwater environment, most targets are small and often obscured, leading to low detection accuracy and missed detections in existing target detection algorithms. To address these issues, we propose an underwater target detection algorithm that balances accuracy and speed. Specifically, we first propose the Differentiable Routing Assistance Sampling Network named (DRASN), where differentiable routing participates in training the sampling network but not in the inference process. It replaces the down-sampling network composed of Maxpool and convolution fusion in the backbone network, reducing the feature loss of small and occluded targets. Secondly, we proposed the Bilateral Attention Synergistic Network (BASN), which establishes connections between the backbone and neck with fine-grained information from both channel and spatial perspectives, thereby further enhancing the detection capability of targets in complex backgrounds. Finally, considering the characteristics of real frames, we proposed a scale approximation auxiliary loss function named (Aux-Loss) and modify the allocation strategy of positive and negative samples to enable the network to selectively learn high-quality anchors, thereby improving the convergence capability of the network. Compared with mainstream algorithms, our detection network achieves 82.9% in mAP@0.5 on the URPC2021 dataset, which is 9.5%, 5.7%, and 2.8% higher than YOLOv8s, RT-DETR, and SDBB respectively. The speed reaches 75 FPS and meets the requirements for real-time performance.

Lightweight multi-scale feature dense cascade neural network for scene understanding of intelligent autonomous platform

Understanding the surrounding environment is an essential part of intelligent autonomous platform to complete autonomous driving or other autonomous tasks. Thereinto, scene understanding is a key technology to understand environmental information. For intelligent autonomous platform, it not only requires high accuracy, but also has a strict requirement for the inference speed. Moreover, it should be able to perform normally in complex environments such as target occlusion, the influence of illumination, and objects at different sizes. For tackling these problems, in this study, a lightweight multi-scale feature dense cascade neural network (LMFDCNet) is proposed in real-time scene understanding in complex scenes. Furthermore, trilateral fusion module is adopted to enhance low-level detailed features or high-level semantic features of each branch to describe the object better in different environments. A multi-scale upsampling module that fuses contextual information at different scales allows objects to be represented at different size. The results indicate that LMFDCNet produces 99.1 % classification accuracy on the tobacco dataset. On the Cityscapes dataset, we achieve 74.2 % Mean Intersection over Union (MIoU) with 13.6 M parameters at the speed of 38.2 FPS on a single 1070Ti card. To test the performance on the real-time application in the real world, LMFDCNet is deployed on the autonomous platform for real-time vision task such as classification of tobacco leaf state in curing process and semantic segmentation in the driving process of autonomous platform. The results reveal that LMFDCNet perform well on the AI embedded device and could be applied into intelligent autonomous platform.

Occlusive target recognition method of sorting robot based on anchor-free detection network

ABSTRACT The rapid development of industry intelligence has promoted the importance of sorting robots, but the shortcomings of sorting tasks are also gradually obvious. The target detection of the sorting robot in the process of sorting and grasping is affected by the anchor frame setting, and its grasping efficiency and recognition effect are low. Therefore, a detection network based on an anchor frame-free regression algorithm is proposed in this study, and a positioning and grasping method of the sorting robot based on the Deep Belief Network (DBN) algorithm is proposed. The algorithm experiment shows that the prediction accuracy of the proposed anchor-free detection network in the PASCAL VOC dataset reaches 92.91, significantly higher than that of other detection networks. In addition, the sorting robot was researched and designed with high accuracy in occlusion target recognition. When the occlusion area reaches 70%, the accuracy rate is as high as 81.26%, which is far higher than other detection schemes. The above results show that in improving the sorting robot’s target recognition ability, the anchor-free detection network can significantly improve its recognition accuracy. At the same time, introducing the DBN network can improve the robot’s recognition ability for occluded targets, which is of great significance to the development of the sorting robot and the intelligent industry.

Accelerometer-aided millimeter-wave radar interferometry for uninterrupted bridge displacement estimation considering intermittent radar target occlusion

Monitoring bridge displacement is necessary to detect early signs of damage, prevent catastrophic failures, and maintain public confidence in the safety of critical transportation infrastructure. Radar interferometry is attractive for long-term continuous monitoring of bridge displacements, given its resilience to weather and lighting conditions. However, intermittent radar target occlusions caused by moving vehicles beneath bridges pose challenges during extended continuous monitoring periods. In this study, accelerometer-aided millimeter-wave radar interferometry was explored with an explicit consideration of intermittent radar target occlusion. Radar and accelerometer measurements were first recorded over a short period. Multiple good targets were selected among all radar-detected targets, and their direction-converting factors were estimated using short-period measurements. Subsequently, bridge displacement was continuously estimated by overcoming intermittent radar target occlusions. A displacement reconstruction algorithm was proposed for target occlusion periods, and a multitarget-based phase-unwrapping algorithm was proposed to remove the displacement drift caused by radar target occlusion. The proposed technique was experimentally validated through laboratory tests on a 10-meter-long bridge structure and field tests on two different spans of a footbridge. The technique accurately estimated displacements with overall errors below 0.1 mm, allowing it to be widely applied to bridges with millimeter-scale displacements.

Siamese Tracking Network with Multi-attention Mechanism

Object trackers based on Siamese networks view tracking as a similarity-matching process. However, the correlation operation operates as a local linear matching process, limiting the tracker’s ability to capture the intricate nonlinear relationship between the template and search region branches. Moreover, most trackers don’t update the template and often use the first frame of an image as the initial template, which will easily lead to poor tracking performance of the algorithm when facing instances of deformation, scale variation, and occlusion of the tracking target. To this end, we propose a Simases tracking network with a multi-attention mechanism, including a template branch and a search branch. To adapt to changes in target appearance, we integrate dynamic templates and multi-attention mechanisms in the template branch to obtain more effective feature representation by fusing the features of initial templates and dynamic templates. To enhance the robustness of the tracking model, we utilize a multi-attention mechanism in the search branch that shares weights with the template branch to obtain multi-scale feature representation by fusing search region features at different scales. In addition, we design a lightweight and simple feature fusion mechanism, in which the Transformer encoder structure is utilized to fuse the information of the template area and search area, and the dynamic template is updated online based on confidence. Experimental results on publicly tracking datasets show that the proposed method achieves competitive results compared to several state-of-the-art trackers.

UAV Visual Tracking with Enhanced Feature Information

Unmanned aerial vehicles (UAVs) visual tracking is an important research direction. The tracking object is lost due to the problems of target occlusion, illumination variation, flight vibration and so on. Therefore, based on a Siamese network, this study proposes a UAVs visual tracker named SiamDFT++ to enhance the correlation of depth features. First, the network width of the three-layer convolution after the full convolution neural network is doubled, and the appearance information of the target is fully utilized to complete the feature extraction of the template frame and the detection frame. Then, the attention information fusion module and feature deep convolution module are proposed in the template branch and the detection branch, respectively. The feature correlation calculation methods of the two depths can effectively suppress the background information, enhance the correlation between pixel pairs, and efficiently complete the tasks of classification and regression. Furthermore, this study makes full use of shallow features to enhance the extraction of object features. Finally, this study uses the methods of deep cross-correlation operation and complete intersection over union to complete the matching and location tasks. The experimental results show that the tracker has strong robustness in UAVs short-term tracking scenes and long-term tracking scenes.

Design and application of UAV single target tracking system based on YOLOv5

In aerial reconnaissance, pursuit, and post-disaster search and rescue, rotorcraft UAVs are valued for their agility. A key challenge is target tracking in complex environments with no prior information, dense obstacles, GNSS signal loss, and occluded targets. This paper introduces a YOLOv5-based UAV tracking system to address detection and path planning. The quadrotor UAV model ensures stable flight through mathematical modeling and simulation. YOLOv5 enhances detection performance with real-time tracking. Feedback control enables autonomous obstacle avoidance and target tracking. Results show adaptability and robustness, supporting reconnaissance and rescue missions effectively.

A Fast and High-Accuracy Foreign Object Detection Method for Belt Conveyor Coal Flow Images with Target Occlusion.

Foreign objects in coal flow easily cause damage to conveyor belts, and most foreign objects are often occluded, making them difficult to detect. Aiming at solving the problems of low accuracy and efficiency in the detection of occluded targets in a low-illumination and dust fog environment, an image detection method for foreign objects is proposed. Firstly, YOLOv5s back-end processing is optimized by soft non-maximum suppression to reduce the influence of dense objects. Secondly, SimOTA label allocation is used to reduce the influence of ambiguous samples under dense occlusion. Then, Slide Loss is used to excavate difficult samples, and Inner-SIoU is used to optimize the bounding box regression loss. Finally, Group-Taylor pruning is used to compress the model. The experimental results show that the proposed method has only 4.20 × 105 parameters, a computational amount of 1.00 × 109, a model size of 1.20 MB, and an mAP0.5 of up to 91.30% on the self-built dataset. The detection speed on the different computing devices is as high as 66.31, 41.90, and 33.03 FPS. This proves that the proposed method achieves fast and high-accuracy detection of multi-layer occluded coal flow foreign objects.

Global remote feature modulation end-to-end detection

Object detector based on fully convolutional network achieves excellent performance. However, existing detection algorithms still face challenges such as low detection accuracy in dense scenes and issues with occlusion of dense targets. To address these two challenges, we propose an Global Remote Feature Modulation End-to-End (GRFME2E) detection algorithm. In the feature extraction phase of our algorithm, we introduces the Concentric Attention Feature Pyramid Network (CAFPN). The CAFPN captures direction-aware and position-sensitive information, as well as global remote dependencies of features in deep layers by combining Coordinate Attention and Multilayer Perceptron. These features are used to modulate the front-end shallow features, enhancing inter-layer feature adjustment to obtain comprehensive and distinctive feature representations.In the detector part, we introduce the Two-Stage Detection Head (TS Head). This head employs the First-One-to-Few (F-O2F) module to detect slightly or unobstructed objects. Additionally, it uses masks to suppress already detected instances, and then feeds them to the Second-One-to-Few (S-O2F) module to identify those that are heavily occluded. The results from both detection stages are merged to produce the final output, ensuring the detection of objects whether they are slightly obscured, unobstructed, or heavily occluded. Experimental results on the pig detection dataset demonstrate that our GRFME2E achieves an accuracy of 98.4%. In addition, more extensive experimental results show that on the CrowdHuman dataset, our GRFME2E achieves 91.8% and outperforms other methods.

Orthogonalized layer features with wavelet for multi-object tracking

Abstract Multi-object tracking (MOT) in crowded scenes presents challenges such as target occlusion and interference from similar objects. The detection models that rely on the target center as positive samples often struggle with noise introduced by ambiguous data annotations. To address these issues, we propose to segregate the target features of distinct frequency tiers by leveraging wavelet decomposition. Features obtained from two-dimensional wavelet decomposition can exhibit orthogonality and complementarity along the horizontal and vertical directions. Low-frequency components are usually related to visible targets, while high-frequency energy often emanates from targets undergoing occlusion, enabling discrimination between multi-layered objectives. Experimental evaluations are conducted on the MOT17 and MOT20 benchmarks (motchallenge.net), which demonstrate that our approach has better competitive performance compared to the current state-of-the-art methods.

A Diffusion Neural Network-Enhanced Object Tracking Approach Under Sports Scenarios

Object tracking for motion scenes is a common research concern in field of computer vision. Its goal is to accurately track targets in different time periods and predict their future states by utilizing the motion information in video sequences. However, traditional target-tracking methods in motion scenes often face challenges such as target blur, occlusion, and changes in lighting. To deal with this issue, this paper proposes a diffusion neural network-enhanced object-tracking approach under sports scenarios. In order to further improve tracking performance, the diffusion convolution operation is introduced, which propagates features at different time steps to enhance the modeling ability of target motion. Then, suitable influencing factors are selected based on motion scene object feature parameters. Finally, a target tracking method is established by integrating these two methods. In the experiment, we used a large number of real motion scene datasets to evaluate the proposed method. The experimental results show that by comparing with traditional moving object tracking methods, the proposal achieves significant improvement in tracking accuracy and methodology robustness. In addition, we also conducted stability experiments, proving that this method has good stability for models with varying kernel numbers.

A Lightweight CER-YOLOv5s Algorithm for Detection of Construction Vehicles at Power Transmission Lines

In the context of power-line scenarios characterized by complex backgrounds and diverse scales and shapes of targets, and addressing issues such as large model parameter sizes, insufficient feature extraction, and the susceptibility to missing small targets in engineering-vehicle detection tasks, a lightweight detection algorithm termed CER-YOLOv5s is firstly proposed. The C3 module was restructured by embedding a lightweight Ghost bottleneck structure and convolutional attention module, enhancing the model’s ability to extract key features while reducing computational costs. Secondly, an E-BiFPN feature pyramid network is proposed, utilizing channel attention mechanisms to effectively suppress background noise and enhance the model’s focus on important regions. Bidirectional connections were introduced to optimize the feature fusion paths, improving the efficiency of multi-scale feature fusion. At the same time, in the feature fusion part, an ERM (enhanced receptive module) was added to expand the receptive field of shallow feature maps through multiple convolution repetitions, enhancing the global information perception capability in relation to small targets. Lastly, a Soft-DIoU-NMS suppression algorithm is proposed to improve the candidate box selection mechanism, addressing the issue of suboptimal detection of occluded targets. The experimental results indicated that compared with the baseline YOLOv5s algorithm, the improved algorithm reduced parameters and computations by 27.8% and 31.9%, respectively. The mean average precision (mAP) increased by 2.9%, reaching 98.3%. This improvement surpasses recent mainstream algorithms and suggests stronger robustness across various scenarios. The algorithm meets the lightweight requirements for embedded devices in power-line scenarios.