YOLOV: Making Still Image Object Detectors Great at Video Object Detection (2208.09686v2)

Abstract: Video object detection (VID) is challenging because of the high variation of object appearance as well as the diverse deterioration in some frames. On the positive side, the detection in a certain frame of a video, compared with that in a still image, can draw support from other frames. Hence, how to aggregate features across different frames is pivotal to VID problem. Most of existing aggregation algorithms are customized for two-stage detectors. However, these detectors are usually computationally expensive due to their two-stage nature. This work proposes a simple yet effective strategy to address the above concerns, which costs marginal overheads with significant gains in accuracy. Concretely, different from traditional two-stage pipeline, we select important regions after the one-stage detection to avoid processing massive low-quality candidates. Besides, we evaluate the relationship between a target frame and reference frames to guide the aggregation. We conduct extensive experiments and ablation studies to verify the efficacy of our design, and reveal its superiority over other state-of-the-art VID approaches in both effectiveness and efficiency. Our YOLOX-based model can achieve promising performance (\emph{e.g.}, 87.5\% AP50 at over 30 FPS on the ImageNet VID dataset on a single 2080Ti GPU), making it attractive for large-scale or real-time applications. The implementation is simple, we have made the demo codes and models available at \url{https://github.com/YuHengsss/YOLOV}.

Exploring Efficiency and Accuracy in Video Object Detection with YOLOV

"YOLOV: Making Still Image Object Detectors Great at Video Object Detection" introduces a novel framework for enhancing video object detection efficiency and accuracy. Leveraging one-stage detectors, the authors address the computational limitations typically associated with two-stage detectors in a video context, while proposing mechanisms to effectively aggregate temporal features from video sequences.

The focus on making single-stage detectors such as YOLOX proficient in handling video data underpins the methodology. Unlike traditional approaches that utilize a two-stage detector model, YOLOV incorporates a strategy to select and enhance relevant features post one-stage detection, thereby avoiding the computational expense of processing a larger number of low-quality candidates typically encountered in a two-stage setup.

One of the core innovations discussed in the paper is the Feature Selection Module (FSM). By employing top confidence predictions and Non-Maximal Suppression (NMS), YOLOV effectively isolates critical features at minimal computational cost. Following this, a Feature Aggregation Module (FAM) is introduced. This module utilizes an affinity-based manner to aggregate features, tackling the common homogeneity issue experienced with the cosine similarity method alone. Here, attention mechanisms adapted for the task play a pivotal role, allowing the framework to derive better classification through enhanced feature aggregation.

Empirical evaluations on the ImageNet VID dataset demonstrate the efficiency of YOLOV. Despite the simplicity of its implementation, YOLOV achieves an impressive 87.5% AP50 while maintaining inference speeds of over 30 FPS on a 2080Ti GPU. This positions YOLOV not only as a credible alternative to two-stage video object detectors in real-time applications but also emphasizes its applicability for large-scale tasks.

A noteworthy claim from the authors is the versatility of the proposed method across different detectors. Experiments reveal consistent improvements over baseline performance when applying YOLOV's strategy to other well-known one-stage models such as PPYOLOE and FCOS.

In the broader landscape of video object detection, the implications of this research reach both theoretical and practical dimensions. On the theoretical side, it challenges existing paradigms by illustrating that video-level feature aggregation can be effectively performed within the more efficient architecture of one-stage detectors. Practically, YOLOV opens pathways for deploying video object detection mechanisms in environments constrained by computational resources, where traditional two-stage processes are impracticable.

Future development possibilities include refining the threshold for average pooling in FAM for dynamically adapting to diverse video contents and exploring more robust sampling strategies for frames to further optimize performance balance between accuracy and computational load.

In conclusion, YOLOV presents a compelling advancement in video object detection, effectively merging the accuracy of sophisticated feature aggregation with the speed advantages intrinsic to one-stage detection models. This research potentially sets a new benchmark for video object detection, providing a viable solution to the constraints of real-time applications in resource-intensive settings.