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在 OpenClaw 中安装
/install dynamic-object-aware-egomotion-egomotion-estimation
功能描述
Estimate camera motion with optical flow + affine/homography, allow multi-label per frame.
使用说明 (SKILL.md)
When to use
- You need to classify camera motion (Stay/Dolly/Pan/Tilt/Roll) from video, allowing multiple labels on the same frame.
Workflow
- Feature tracking:
goodFeaturesToTrack+calcOpticalFlowPyrLK; drop if too few points. - Robust transform:
estimateAffinePartial2D(or homography) with RANSAC to get tx, ty, rotation, scale. - Thresholding (example values)
- Translate threshold
th_trans(px/frame), rotation (rad), scale delta (ratio). - Allow multiple labels: if scale and translate are both significant, emit Dolly + Pan; rotation independent for Roll.
- Translate threshold
- Temporal smoothing: windowed mode/median to reduce flicker.
- Interval compression: merge consecutive frames with identical label sets into
start->end.
Decision sketch
labels=[]
for each frame i>0:
lbl=[]
if abs(scale-1)>th_scale: lbl.append("Dolly In" if scale>1 else "Dolly Out")
if abs(rot)>th_rot: lbl.append("Roll Right" if rot>0 else "Roll Left")
if abs(dx)>th_trans and abs(dx)>=abs(dy): lbl.append("Pan Left" if dx>0 else "Pan Right")
if abs(dy)>th_trans and abs(dy)>abs(dx): lbl.append("Tilt Up" if dy>0 else "Tilt Down")
if not lbl: lbl.append("Stay")
labels.append(lbl)
Heuristic starting points (720p, high fps; scale with resolution/fps)
- Tune thresholds based on resolution and frame rate (e.g., normalize translation by image width/height, rotation in degrees, scale as relative ratio).
- Low texture/low light: increase feature count, use larger LK windows, and relax RANSAC settings.
Self-check
- Fallback to identity transform on failure; never emit empty labels.
- Direction conventions consistent (image right shift = camera pans left).
- Multi-label allowed; no forced single label.
- Compressed intervals cover all sampled frames; keys formatted correctly.
安全使用建议
This skill is internally coherent and does not request credentials or perform network actions. Before installing: (1) understand that SKILL.md is a design/algorithm sketch, not runnable code — you (or the agent runtime) will need appropriate libraries (e.g., OpenCV, a Python runtime) to implement it; (2) confirm where and how video frames will be provided and whether any sensitive video would be processed or uploaded by your agent; (3) if you expect a packaged implementation, prefer a skill that declares exact dependencies or includes code/verifiable sources. Overall it appears fine from a security/permission perspective.
功能分析
Type: OpenClaw Skill
Name: dynamic-object-aware-egomotion-egomotion-estimation
Version: 0.1.0
The skill bundle contains standard instructions and pseudocode for camera egomotion estimation using computer vision techniques (optical flow, RANSAC, and affine transforms). The SKILL.md file describes a legitimate workflow for classifying video motion (Pan, Tilt, Dolly, etc.) and lacks any indicators of malicious intent, data exfiltration, or harmful prompt injection.
能力评估
Purpose & Capability
Name/description align with the instructions: feature tracking, RANSAC-based affine/homography estimation, thresholding and temporal compression. The only minor mismatch is that the SKILL.md does not declare required runtime dependencies (e.g., OpenCV, Python/runtime), which are necessary to implement the described functions but are not security-sensitive.
Instruction Scope
The instructions are narrowly scoped to processing video frames (feature detection, optical flow, transform estimation, label heuristics) and do not ask the agent to read unrelated files, access environment secrets, or transmit data to external endpoints.
Install Mechanism
No install spec (instruction-only), so nothing is written to disk or fetched during install — lowest install risk.
Credentials
The skill requests no environment variables, credentials, or config paths. That is proportionate to its stated local-video-processing purpose.
Persistence & Privilege
The skill is not forced-always and does not request elevated persistence or modify system/other-skill configs. Agent autonomous invocation is allowed (platform default) but not by itself a concern here.
如何使用
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install dynamic-object-aware-egomotion-egomotion-estimation - 安装完成后,直接呼叫该 Skill 的名称或使用
/dynamic-object-aware-egomotion-egomotion-estimation触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.1.0
Bulk publish from all-task-skills-dedup
元数据
常见问题
egomotion-estimation 是什么?
Estimate camera motion with optical flow + affine/homography, allow multi-label per frame. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 76 次。
如何安装 egomotion-estimation?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install dynamic-object-aware-egomotion-egomotion-estimation」即可一键安装,无需额外配置。
egomotion-estimation 是免费的吗?
是的,egomotion-estimation 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
egomotion-estimation 支持哪些平台?
egomotion-estimation 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 egomotion-estimation?
由 wu-uk(@wu-uk)开发并维护,当前版本 v0.1.0。
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