CCTV ゲームの仕組み

CCTV ゲームの仕組み

CCTV games are a category of prediction-based betting products built on live surveillance footage and AI-powered object detection. Instead of spinning reels or drawing cards, you predict how many real-world objects - cars, pedestrians, ducks, skiers - will pass through a defined detection zone during a fixed time window. The outcome is determined by what actually happens in the physical world, counted by machine learning models in real time.

This guide covers everything: how rounds are structured, how the AI works, what the bet types mean、そして how the camera network operates.

フォーマット

CCTV games were developed by 155.io, a Swedish gaming studio. The product launched in January 2026 with its first title, Rush Hour, a vehicle-counting game set across major global cities. The concept is straightforward: take real surveillance infrastructure, apply computer vision to count objects with precision、そして build a betting market around the count.

The studio's CEO, Sam Jones, described the concept as "Think Big Brother blended with Polymarket" - a phrase that captures both the surveillance aesthetic と prediction-market structure at the core of each round.

155.io distributes its games through Hub88, an aggregation platform that connects game studios to online casino operators. This means CCTV games are available at Roobet、Stake、Shuffle、そして any other Hub88-integrated platform that has elected to carry the content.

ステップバイステップのラウンドウォークスルー

Every round in a CCTV game follows the same three-phase structure: the betting window, the counting phase、そして settlement. アンダーstanding each phase is essential before placing a bet.

フェーズ1 - ベットウィンドウ

The betting window opens at the start of each round, typically lasting 15 to 30 seconds depending on the title. During this window, you can see the live camera feed and observe current conditions - traffic density, pedestrian volume, weather - before committing to a bet. The active camera location and current round parameters are displayed on screen.

Odds are fixed at round open and do not change during the betting window, regardless of what the camera is showing. This means you can observe conditions without worrying about odds moving against you as you decide.

Bet types とir payouts are available simultaneously: オーバー, アンダー, レンジ、そして イグザクト. You can place multiple bet types in a single round if the platform permits parlays on the same event, though this varies by operator.

フェーズ2 - カウントフェーズ

Once the betting window closes, the counting phase begins. The camera feed remains live and visible. An AI model - running computer vision on the video stream - draws bounding boxes around objects as they cross the detection zone. Each confirmed crossing increments the round counter, which is displayed in real time.

The counting phase typically lasts 60 seconds. Throughout this window, you can watch the count build in real time. Objects that enter the detection zone but do not fully cross it are not counted. Objects counted include only those that pass through the defined zone in the designated direction (where directional filtering is active).

The visual experience during the counting phase is a key differentiator of the format. Unlike a slot spin that resolves in 2-3秒, you are watching a real event unfold over a full minute、と a running count visible the entire time.

フェーズ3 - 決済

At the end of the counting window, the final count is locked. Settlement occurs immediately and automatically. Winning bets are credited to your account balance. The next round's betting window opens within seconds.

Settlement is deterministic: the AI's final count is the outcome. There is no human adjudication. If the AI counted 7 vehicles and your レンジ bet covered 5-8, you win. If it counted 12, you lose. The count displayed during the round is the count used for settlement - there is no post-round adjustment.

カメラロケーション and Rotation System

Rush Hour, the flagship title, operates across a network of city cameras spanning multiple continents and time zones. The rotation system ensures that regardless of when you play, at least some locations are experiencing active traffic conditions.

Current camera cities in the Rush Hour network include:

• 東京、日本
• ニューヨーク、米国
• ロンドン、英国
• バンコク、タイ
• パリ、フランス
• シドニー、オーストラリア

Additional cities are integrated on a rolling basis as 155.io expands its camera partnerships. Each camera is positioned to capture a high-traffic zone - typically an intersection, a bridge crossing、または a pedestrian thoroughfare - where consistent object volume makes for meaningful rounds.

The rotation system cycles between cameras at defined intervals. Some platforms display the upcoming camera so you can anticipate conditions before the betting window opens. Local time at each camera location is relevant: a 東京 camera during rush hour will produce different counts than the same camera at 3am local time.

For Duck River, cameras are positioned at river locations where waterfowl movement is predictable but not uniform - creating natural variance around a baseline. For Snow Run, mountain resort infrastructure provides the camera positions.

検知ゾーンメカニクス

The detection zone is a defined area within the camera frame. Only objects that fully cross this zone are counted. This is the most important technical concept to understand, because partial crossings, objects that stop before the zone、そして objects that cross outside the zone boundary all result in zero contribution to the count.

カウント対象

• Objects that enter the detection zone boundary and fully exit the other side
• Objects detected with confidence above the model's threshold (typically 85-90%)
• Objects of the designated type for that game (vehicles in Rush Hour, waterfowl in Duck River, skiers in Snow Run)

カウントされないもの

• Objects that enter the zone but stop or reverse before exiting
• Objects that pass adjacent to the zone without crossing the boundary
• Objects of the wrong category (a cyclist in a vehicle-only zone, for example)
• Objects below the confidence threshold - these are flagged as uncertain and excluded
• Objects partially occluded to the point where the model cannot confirm category

The detection zone is visible as an overlay on the camera feed during the counting phase. This transparency is intentional - you can see exactly what area the AI is monitoring、そして you can observe in real time whether objects are crossing or not.

AI信頼スコアの説明

The AI model running object detection produces a confidence score for every detection event. A score of 94% means the model assigns 94% probability that the detected object is of the correct category - a vehicle, a duck, a skier - and that it has correctly located its bounding box.

信頼閾値

155.io's models operate with a minimum confidence threshold before a detection is counted. Objects detected below this threshold are discarded. This creates a small category of edge cases: objects that are partially visible, heavily occluded、または ambiguously categorized may be excluded even if a human observer would count them.

In practice, the threshold is calibrated to minimize both false positives (counting something that is not there) and false negatives (missing something that is there). The tradeoff is that rare legitimate crossings are occasionally excluded. This is reflected in the RTP and payout structure - see RTPとハウスエッジの説明 詳細については。

バウンディングボックス

バウンディングボックス are the rectangular overlays drawn around each detected object. They are visible on the live feed during the counting phase. A box that appears とn crosses the detection zone line contributes to the count. Watching bounding box behavior is one way to develop intuition for how the model behaves at specific camera locations.

Edge cases with bounding boxes include:

• Box merging: two adjacent objects detected as one, resulting in one count instead of two
• Box splitting: one object momentarily tracked as two, though duplicate-detection logic prevents double-counting in most cases
• Box dropout: the model briefly loses track of an object mid-crossing; depending on implementation, this may or may not complete the count

ベットタイプの詳細

Every CCTV game offers four core bet types. アンダーstanding the probability and payout of each is essential for informed play.

ベットタイプ	払い戻し乗数	概算確率	説明
オーバー	3.60x	~25%	Final count exceeds the set threshold
アンダー	3.00x	~30%	Final count falls below the set threshold
レンジ	2.25x	~40%	Final count falls within a specified range
イグザクト	18.00x	~5%	Final count matches an exact number

オーバーベット

The オーバー bet pays 3.60x on a win. You are predicting that the object count will exceed a threshold set by the platform for that round. At a roughly 25% win probability, this is a lower-frequency but higher-payout option. Example: the threshold is set at 10 vehicles. You bet オーバー. The AI counts 14 vehicles. You win 3.60x your stake.

The オーバー bet performs best when you observe dense, fast-moving conditions during the betting window - heavy traffic, crowded crossings, high activity on the slope.

アンダーベット

The アンダー bet pays 3.00x and wins at approximately 30% frequency. You are predicting a count below the set threshold. This is the natural counterpart to オーバー、そして its slightly lower payout reflects its marginally higher win probability. Example: threshold is 10 vehicles. You bet アンダー. The AI counts 7 vehicles. You win 3.00x your stake.

レンジベット

The レンジ bet pays 2.25x and wins approximately 40% of the time. You select a range of acceptable counts - for example, 5-9 vehicles - and win if the final count falls within it. This is the most conservative of the four bet types, offering the most frequent wins at the cost of lower payout per win.

レンジ bets suit conditions where the activity level is readable but not extreme - not clearly very high or very low, but somewhere in the middle band. Example: you observe moderate traffic and predict 5-9 vehicles. The AI counts 7. You win 2.25x your stake.

イグザクトベット

The イグザクト bet pays 18.00x and wins approximately 5% of the time. You predict an exact count. This is the highest-variance, highest-payout option and carries the highest house edge. Example: you predict exactly 6 vehicles. The AI counts 6. You win 18.00x your stake.

The イグザクト bet is best understood as a longshot, not a regular strategy. Its 18x payout is attractive, but the 5% win rate and elevated house edge mean expected value is lower than オーバー or アンダー. See RTPガイド 完全な内訳については。

カウント分布パターン

Object counts are not uniformly distributed. They follow patterns shaped by real-world conditions: time of day, day of week, weather, local events, と specific camera location. アンダーstanding distribution tendencies is one way to bring observation into your betting decisions.

In general, Rush Hour city cameras show:

• 高er counts during local peak hours (morning and evening commute windows)
• Lower counts in overnight and early morning periods
• Predictable low-count periods on weekends at business-district cameras
• 高er variance in cameras positioned at arterial routes versus side streets

Counts vary significantly by location. A 東京 intersection during rush hour will produce a very different distribution than the same hour in シドニー. Counts at any given camera also show round-to-round variance - even during peak periods, individual 60-second windows can produce counts significantly above or below the session average.

なし of this constitutes a reliable edge. The house margin is built into the odds regardless of observable conditions. But observational awareness of baseline activity levels at a given camera can inform which bet type is appropriate - レンジ during moderate conditions, オーバー or アンダー during clearly extreme conditions.

Snow Run用マルチカメラフォーマット

Snow Run introduces a multi-angle format not present in Rush Hour or Duck River. Each round uses three simultaneous feeds:

• POVカメラ: mounted at the base of the slope, facing upward toward incoming skiers
• ドローンカメラ: aerial overhead view of the full slope segment
• 斜面側カメラ: fixed position at the mid-slope crossing point, the primary detection zone

All three feeds are visible simultaneously, but the count is derived from the slope-side camera's detection zone. The POV and drone feeds provide context - you can see skiers approaching and assess density before they reach the counting zone. This creates a brief informational window during the betting phase that is unique to Snow Run.

The multi-camera approach also enhances the spectator experience, making Snow Run particularly suited to streaming and shared viewing.

歴史と配信

155.io was founded in Sweden and developed CCTV games as a novel format for the online gaming market. The studio's background is in computer vision and real-time data processing - the gaming application emerged from an existing capability in AI-based video 分析.

Rush Hour launched in January 2026 as the first commercial title. Duck River and Snow Run followed as the studio expanded its object categories beyond vehicle counting. The distribution model relies on Hub88, which aggregates game content for online casino operators. Hub88's network includes Roobet、Stake、そして Shuffle as anchor platforms、と additional operators integrating the content on an ongoing basis.

見つける the full list of platforms carrying CCTV games at プレイ場所.

How CCTV ゲーム Compare to Other ライブ ゲーム Formats

特徴	CCTV ゲーム	ライブディーラー（ブラックジャック/ルーレット）	バーチャルスポーツ	クラッシュゲーム
結果の源泉	現実世界のイベント	物理的なカード/ボール	RNGアニメーションシミュレーション	RNG乗数
ラウンド時間	~75-90秒	30-120秒	60-180秒	10-60秒
観察入力	はい - ライブ状況が見える	最小限	なし	なし
検証可能性	AIカウントがリアルタイムで表示	物理的プロセスが見える	検証不可	証明可能なフェアハッシュ
共有体験	はい - 同じフィード、同じラウンド	はい - 共有テーブル	No	はい - 共有乗数
ストリーム適性	高	中	Low	高

The key distinction of CCTV games versus virtual sports is real-world grounding. Virtual sports use RNG to animate simulated events. CCTV games use actual surveillance footage and actual events - the vehicles on the screen are real vehicles, the count is a real measurement, not a number generated by an algorithm.

Compared to live dealer games, CCTV games offer faster round pacing and a more visually distinct format. The camera network rotation means every round looks different. ライブ dealer games, by contrast, involve a fixed studio environment with predictable visual elements.

よくある質問

AIカウントは操作できますか？

The AI model runs on 155.io's infrastructure and produces counts from live footage. The process is automated with no human intervention. 155.io publishes information about the detection architecture, と bounding box overlay visible during rounds allows you to observe exactly what is being counted. Platform operators rely on the same count outputs - there is no mechanism for post-hoc adjustment.

Are all players watching the same round?

Yes. All players at any operator carrying the same CCTV game title see the same camera feed と same count in real time. The round is a shared event - there is no individualized feed. This is one reason CCTV games perform well in streaming contexts.

プレイに最適な時間はありますか？

Camera activity varies by local time at each location. You can observe current conditions during the betting window. There is no time of day that reliably produces better expected value - the house edge is embedded in the odds and is constant. But if your preferred bet type is オーバー, playing when cameras are showing high-activity periods increases the likelihood of high counts, which may align with your preference.

勝つ戦略はありますか？

There is no strategy that overcomes the house edge. Observation of camera conditions can inform bet type selection (レンジ when conditions are moderate, オーバー/アンダー when conditions are clearly extreme), but this is not a guarantee of improved outcomes. Every round carries an independent probability distribution. See RTPとハウスエッジの説明 数学的フレームワークについては。

What happens if the camera feed drops mid-round?

Feed interruptions are handled by the platform's terms of service. In most implementations, a round where the feed is interrupted for more than a defined duration is voided and stakes returned. 確認 specific rules at your platform of choice.

さらにタイトルが来ますか？

155.io has indicated an expanding content roadmap beyond the initial three titles. The camera network can support new game types wherever consistent, countable objects are present. Additional urban, natural、そして sporting environments are all plausible directions for new titles.

次のステップ

1. 確認 individual title guides to understand game-specific mechanics: Rush Hour, Duck River, Snow Run
2. 読む RTPとハウスエッジガイド to understand expected value before placing bets
3. 見つける the platforms currently offering CCTV games at プレイ場所
4. から始める レンジ bets to familiarise yourself with round pacing and count behaviour before moving to higher-variance bet types

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