DirectMHP: Direct 2D Multi-Person Head Pose Estimation with Full-range Angles (2302.01110v2)

Abstract: Existing head pose estimation (HPE) mainly focuses on single person with pre-detected frontal heads, which limits their applications in real complex scenarios with multi-persons. We argue that these single HPE methods are fragile and inefficient for Multi-Person Head Pose Estimation (MPHPE) since they rely on the separately trained face detector that cannot generalize well to full viewpoints, especially for heads with invisible face areas. In this paper, we focus on the full-range MPHPE problem, and propose a direct end-to-end simple baseline named DirectMHP. Due to the lack of datasets applicable to the full-range MPHPE, we firstly construct two benchmarks by extracting ground-truth labels for head detection and head orientation from public datasets AGORA and CMU Panoptic. They are rather challenging for having many truncated, occluded, tiny and unevenly illuminated human heads. Then, we design a novel end-to-end trainable one-stage network architecture by joint regressing locations and orientations of multi-head to address the MPHPE problem. Specifically, we regard pose as an auxiliary attribute of the head, and append it after the traditional object prediction. Arbitrary pose representation such as Euler angles is acceptable by this flexible design. Then, we jointly optimize these two tasks by sharing features and utilizing appropriate multiple losses. In this way, our method can implicitly benefit from more surroundings to improve HPE accuracy while maintaining head detection performance. We present comprehensive comparisons with state-of-the-art single HPE methods on public benchmarks, as well as superior baseline results on our constructed MPHPE datasets. Datasets and code are released in https://github.com/hnuzhy/DirectMHP.

• The paper introduces DirectMHP, an end-to-end model that jointly detects and estimates full-range head orientations in cluttered multi-person scenes.
• It leverages novel datasets AGORA-HPE and CMU-HPE to address challenges from occlusions, truncated views, and variable illumination.
• Experiments demonstrate that the method outperforms state-of-the-art approaches by effectively integrating detection with pose regression.

An Examination of "DirectMHP: Direct 2D Multi-Person Head Pose Estimation with Full-range Angles"

The paper "DirectMHP: Direct 2D Multi-Person Head Pose Estimation with Full-range Angles" presents a novel approach to multi-person head pose estimation (MPHPE) in 2D images, with a focus on full-range angles. The authors identify the limitations of existing head pose estimation (HPE) methods that predominantly concentrate on single-person scenarios with detectable front-facing heads. These conventional methods, often reliant on face detection, struggle with arbitrary head orientations and occlusions, limiting their applicability in complex, real-world contexts involving multiple individuals.

Data Challenges and Benchmarks

To support the objectives of their paper, the authors develop two novel and challenging datasets, AGORA-HPE and CMU-HPE, built on existing resources like the AGORA and CMU Panoptic datasets. These datasets introduce numerous challenges, including truncated, occluded, and variably illuminated heads. The authors' emphasis on dataset creation underscores the scarcity of public datasets capable of supporting full-range MPHPE, particularly those capturing environments rich in occlusions or unconventionally oriented heads.

Methodological Contributions

The cornerstone of their approach is the DirectMHP model, an end-to-end trainable one-stage network architecture designed to jointly regress both the locations and orientations of multiple heads. This unified detection and pose estimation mechanism treats head pose as an auxiliary attribute appended to traditional object prediction tasks. Euler angles, among other representations, can be incorporated flexibly due to this architectural design, allowing the simultaneous optimization of head detection and head pose estimation through shared features and losses.

The proposed method seeks to enhance estimation accuracy by utilizing a wider context gleaned from the scene, contrasting with methods that process isolated heads and thereby often miss important contextual cues. Their experimental setup rigorously tests DirectMHP against both novel datasets and existing benchmarks, including comparisons with state-of-the-art approaches which highlight the effectiveness and efficiency of their model.

Results and Implications

The DirectMHP achieves compelling performance metrics, demonstrating superior pose estimation capabilities on the newly constructed datasets. Notable points include its high precision in detecting connected head orientations, indicated by the ability to manage a diversity of head positions without a priori face detection stages.

The paper's findings highlight the potential shift in head pose estimation towards methodologies that integrate detection and orientation estimation tasks in a holistic manner. By promoting a dataset-agnostic end-to-end strategy, it implies significant simplifications for real-world applications, ranging from surveillance in crowded environments to interactive systems that require robust human-computer interaction functionalities.

Future Directions

Despite the positive results, the authors acknowledge the need for further research, particularly in improving the generalization capability of their methods across diverse datasets and in-the-wild scenarios. Future work might focus on addressing challenges related to varying lighting conditions and head orientations that still pose difficulties. Additionally, expanding the dataset resources could further enhance the robustness of the approach in even more varied environmental contexts.

In sum, the work presents a foundational step towards more versatile and robust multi-person head pose estimation systems by leveraging direct end-to-end network training, opening avenues for further exploration and development in both practical applications and theoretical advancements in the field of computer vision.